PR Statistics

ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIAPR)
2023-12-04 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Course Format
Pre-Recorded

Course Details
This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms, how they work, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts.
By the end of this course, students should be able to:

Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
Apply these techniques to real-world datasets and interpret the results.
Understand the underlying methods and assumptions/drawbacks of these techniques.

Intended Audiences
Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful.

Venue
Delivered remotely

Course Information
Availability – TBC
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors.

Assumed quantitative knowledge
A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests, Gradient Boosting, k-NN, SVMs.

Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1
DAY 1
Deep Dive into Supervised Learning
We begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs), exploring their architecture, their use in image and video processing, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing.
In the afternoon sessions we implement CNNs and RNNs using real data sets
R Packages used: keras, tensorflow

Day 2
DAY 2
Advanced Supervised Learning Techniques
On day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture, its self-attention mechanism, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks, and compare it to traditional machine learning methods.
In the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts.
R Packages: keras, tensorflow, rstan, brms, BART

Day 3
DAY 3
Unsupervised Learning – Clustering and Dimension Reduction
The third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering, DBSCAN, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions.
In the afternoon session students will explore the use of these techniques through real-world data sets.
R Packages: cluster, dbscan, factoextra, Rtsne, umap

Day 4
DAY 4
Unsupervised Learning – Anomaly Detection and Course Wrap-up
On the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection, network security, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning.
In the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor.
R Packages: anomalize, forecast, e1071

Dr. Andrew Parnell

Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science, Nature Communications, and Proceedings of the National Academy of Sciences, and has methodological publications in journals such as Statistics and Computing, Journal of Computational and Graphical Statistics, The Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics, time series modelling, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change, 3D printing, and bioinformatics.
Research Gate
Google Scholar
ORCID
LinkedIn
GitHub

ONLINE COURSE – The Practice of RADseq: Population Genomics Analysis with Stacks (RADS02) This course will be delivered live
2023-10-02 - 2023-10-06 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, October 2nd, 2023

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
This course is aimed at introducing researchers to the theory and practice of using reduced representation libraries – such as RAD sequencing – to preform population genomic analysis in non-model organisms. The course will center on running the software pipeline Stacks, focusing on how the characteristics of the underlying molecular libraries result in weak or robust analytical results. Sessions will be live online, consisting of a blend of lectures, interactive demonstrations, and lab practicals, where participants will have the opportunity to ask questions throughout. Computation will be done on the Amazon AWS Cloud.
By the end of the course, participants should be able to:

Navigate the UNIX file system, execute commands, and interact with bioinformatic data files;
Understand how to perform a de novo analysis – without a reference genome – including parameter optimization;
Understand how PCR duplicates and other molecular library characteristics affect analysis;
Complete a reference genome-based analysis;
Take the outputs from Stacks to complete a Structure analysis (de novo), a genome scan based on FST(reference-based), and a private allele analysis.

Intended Audiences
Graduate students, post-doctoral researchers, or professionals who wish to learn how to analyze genomic RAD-based data.

Venue
Delivered Remotely

Course Details
Availability – TBC
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Data and analytical approaches will be presented in a lecture format to introduce key concepts. In the beginning, participants will work interactively with the instructor to understand fundamentals. Once completed, the course will shift into a lab practical format, where the instructor introduces the lab, then free time is given for participants to complete the lab with the instructor present to answer questions. At the end of each practical, the instructor will go over the key ideas and results.

Assumed quantitative knowledge
Basic understanding of evolution (mutation, drift, selection, migration, HWE) and population-genomic concepts (e.g., FST, population structure) is assumed.

Assumed computer background
No computational background knowledge is assumed, however, experience in UNIX and/or bioinformatics analysis will enable participants to move at a faster pace.

Equipment and software requirements
Students will need a laptop or desktop with a fast and reliable internet connection. The computer can run any operating system including MacOS, Windows, or Linux, as we will connect, via the terminal, to our AWS instance on the Amazon Cloud.

Tickets

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PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1: 09:00 – 16:00 (Central Standard Time, i.e., Chicago)

Instructor and participant introductions
Lecture: Exploring the Genetics of Non-Model Organisms with RAD-seq
Introduction to the Cloud
Introduction to UNIX, Part 1

Day 2: 09:00 – 16:00

Short Lecture: Illumina error model, FastQ files, data quality control, and sample multiplexing
Cleaning and demultiplexing RAD-seq data
Introduction to UNIX, Part 2
Participant two-minute lightning talks

Day 3: 09:00 – 16:00

Short Lecture: Parameter optimization and de novo assembly of RAD tags
Understanding the de novo assembly algorithm
How to optimize assembly parameters in a de novo assembly
De novo assembly of RAD tags without a genome for a STRUCTURE Analysis

Day 4: 09:00 – 16:00

Referenced-aligned RAD tags for genome scanning and identifying signatures of selection
How to perform an integrated analysis – applying de novo data to a related reference genome

Day 5: 09:00 – 16:00

Short Lecture: Understanding DNA quality, molecular library integrity, and PCR duplicates
Examining the effects of PCR duplicates in a bird dataset
Performing a private allele analysis in a hybrid zone
Open lab, time for questions on participant provided data sets.

Course Instructor

Dr. Julian Catchen is an Associate Professor at the University of Illinois at Urbana-Champaign where he runs a population genomics lab that focuses on how the evolution of the genome affects underlying genomic architecture. He is the primary author of Stacks and has been involved in RADseq analysis since 2009, working on projects in a variety of fishes, birds, and insects while applying a diversity of genomic analyses including defining population structure, conducting genome scans, private allele analysis, and phylogenetics.
Lab Website: https://catchenlab.life.illinois.edu/Google Scholar: https://scholar.google.com/citations?user=YKnVJaAAAAAJ&hl=enResearch Gate: https://www.researchgate.net/profile/Julian-CatchenORCID: https://orcid.org/0000-0002-4798-660X

ONLINE COURSE - Advanced Ecological Niche Modelling (ENM/SDM) Using R (ANMR02) Deadline to register 28th August
2023-09-11 - 2023-09-15 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, September 11th, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – UTC – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Have you built an Ecological Niche Model? If yes, you have already encountered challenges on data preparation, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for mechanistic and correlative models, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental, topographical, human) to species physiological data or geographical locations, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management.
By the end of 5-day practical course you will have the capacity to

filter records and select your variables with variance inflation factor;
test the effect of Maxent regularization parameter in models performance;
validate models performance and accuracy;
perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.

Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Intended Audiences
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines, with experience in ecological niche models.

Venue
Delivered remotely

Course Details
Availability – 24 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3ECT’s
Language – English

Teaching Format
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Assumed quantitative knowledge
A basic understanding of ecological niche models and biogeography in general is required, thus we will assume the attendees know how to run an ecological niche model.

Assumed computer background
Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models, please consider attending our basic course on PRStatistics (www.prstats.org).

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 11th
Classes from 09:30 to 17:30
Day One:

ENM guide: how to model
ENM R packages.
Sources of environmental variables using geodata package.
Getting species records with geodata package.

Tuesday 12th
Classes from 09:30 to 17:30
Day Two:

Variable selection with variance inflation factor (VIF) and usdm packages.
Choosing the correct study area.
Filtering records using usdm/spThin packages.
Choosing pseudo-absences with Biomod2 package.

Wednesday 13th
Classes from 09:30 to 17:30
Day Three:

Split records in training and test with ENMeval package.
Test effect of Maxent regularization parameter.
Comparing correlative models with AIC, with ENMeval package.

Thursday 14th
Classes from 09:30 to 17:30
Day Four:
 MESS practice with Biomod2 package.
 Validate models null models.
 VirtualSpecies virtualspecies packages.

Friday 15th
Classes from 09:30 to 17:30
Day Five:

Mechanistic model NicheMapper packages.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Dr. Salvador Arenas-Castro

Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology, forestry, spatial ecology, andecoinformatics, all addressed by explicitly considering the spatial component ofecological processes, mainly applying spatially explicit modelling approaches, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com
Google Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro

ONLINE COURSE - Introduction to Bayesian modelling with INLA (BMIN02) This course will be delivered live
2023-05-22 - 2023-05-26 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, May 22nd, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics on the INLA methodology as well as practical modelling of different types of data.
By the end of the course participants should:

Understand the basics of Bayesian inference.
Understand how the INLA method works and its main differences with MCMC methods.
Be able to fit models with the R-INLA package.
Know how to interpret the output from model fitting.
Be confident with the use of INLA for data analysis.
Understand the different models that can be fit with INLA.
Know how to define the different parts of a model with INLA.
Be able to develop new latent effects not implemented in the R-INLA package.
Know how to define new priors not included in the R-INLA package.
Have the confidence to use INLA for their own projects.

Intended Audiences
Academics and post-graduate students working on projects related to data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.

Venue
Venue – Delivered remotely

Course Information
Time zone – Central European Standard Time (CEST)
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

Teaching Format

he course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference.
Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful.
Equipment and software requirements
A laptop/personal computer with any operating system (Linux, Windows, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
https://cran.r-project.org/

Download RStudio

UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).

Assumed computer background
Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed.

Equipment and software requirements
Attendees will need to install/update R/RStudio and various additional R packages.
This can be done on Macs, Windows, and Linux.
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
info@clovertraining.co.uk

Monday 22nd
Classes from 14:00 to 21:00
Introduction to the courseKey concepts related to Bayesian inferenceModels with conjugate priorsIntroduction to Bayesian hierarchical modelsComputational methods for Bayesian inferenceIntroduction to the INLA methodologyFitting generalized linear models with INLA and the R-INLA packageUnderstanding and manipulating the output from model fitting with R-INLA

Tuesday 23rd
Classes from 10:00 – 17:00Fitting generalized linear mixed models with R-INLATypes of latent effects in R-INLAModels with i.i.d. latent effectsFitting multilevel models with R-INLAModels with correlated latent effectsFitting time series models with R-INLA

Wednesday 24th
Classes from 14:00 – 21:00Priors in R-INLASetting priors in R-INLAIntroduction to Penalized Complexity priors (PC-priors)Defining new priors in R-INLASpatially correlated random effectsFitting spatial models with R-INLAVisualizing the output from spatial models and mapping

Thursday 25th
Classes from 14:00 – 21:00Advanced features in R-INLAComputing linear combinations of the latent effectsFitting models with several likelihoodsModels with shared termsAdding linear constraints to the latent effectsImplementing new latent models in R-INLAImputation and missing covariates in R-INLA

Friday 26th
Classes from 14:00 to 21:00
Case studies and own data.

Course Instructor

Dr Virgillio Gomez Rubio
Works at: Universdad de Castilla~La Mancha
Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference withINLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on his website.

Researchgate: https://www.researchgate.net/profile/Virgilio-Gomez-Rubio?ev=hdr_xprf
Google Scholar: https://scholar.google.es/citations?user=OggVQCkAAAAJ&hl=es
ORCID: https://orcid.org/0000-0002-4791-3072
GitHub: https://github.com/becarioprecario
Homepage: https://becarioprecario.github.io

ONLINE COURSE – A Non Mathematical Introduction To Ordination Methods Using R (ORDM01) Registration deadline 27th February - This course will be delivered live
2023-03-27 - 2023-03-30 | Delivered remotely (Canada)
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, March 27th, 2023

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – EST – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
This course will introduce you to the main methods of both constrained and unconstrained ordination without entering into the mathematical details of these methods. The following methods will be studied: principal component analysis; correspondence analysis and its detrended version; principal coordinates analysis; metric and non-metric multidimensional scaling; redundancy analysis, canonical correspondence analysis; gradient analysis using siteXspecies data.
By the end of the course, participants should be able to:

Understand how each method works and the assumptions inherent in each;
Choose the most appropriate method relative to their data and goals;
Carry out the analyses in the R statistical environment
Interpret their results

Intended Audiences

Graduate or post-doctoral level researchers who wish to learn how to perform ordination techniques in R;
Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data

Venue
Delivered remotely

Course Details
Time Zone – EST
Availability – TBC
Duration – 4 days
Contact hours – Approx. 30 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously.

Assumed quantitative knowledge
I assume that participants have a basic knowledge of general statistical concepts and of linear models.

Assumed computer background
Experience with performing statistical analyses using R and R Studio will be assumed.

Equipment and software requirements
A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.
A full list of required packages will be made available to participants prior to the course.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously.

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 27th
Day 1 09:00 – 16:00
Principal components analysis (PCA)

A graphical explanation of how PCA works
Data preparation and basic assumptions
Dealing with proportions
Using a covariance matrix or a correlation matrix?
Steps in fitting a PCA
Evaluating the importance of each axis
Relating variables to the axes
Relating observations to the axes
Choosing which axes to use
Graphical visualizations using biplots

Correspondence analysis (CA) & detrended correspondence analysis (DCA)
Direct gradient analysis in ecology
CA as a form of direct gradient analysis
Steps in fitting a CA
Bias due to the “arch effect” and its correction by detrending
An empirical example
Interpreting the output and graphical presentation

Tuesday 28th
Day 2 09:00 – 16:00
Principal coordinates analysis (PCoA)

Distance and dissimilarity measures
Measures for nominal categorical, binary, ordinal and quantitative variables
Gower’s distance
PCA and CA as special cases of multidimensional scaling
A graphical explanation of how PCoA works
Steps in fitting a CA
Performing PCoA in R

Metric (MDS) and non-metric multidimensional scaling (NMDS)
What is multidimensional scaling and how does it work?
What is non-metric multidimensional scaling?
Performing NMDS in R.
Graphical methods for evaluating and interpreting NMDS results
Procrustes analysis
An empirical example

Wednesday 29th
Day 3 09:00 – 16:00
Constrained ordinations
Exploratory vs. inferential statistical methods
Redundancy analysis (RDA)
Obtaining output from rda()
Hypothesis testing with rda()
Partial RDA
Canonical correspondence analysis (CCA)
Partial CCA
Hypothesis testing with CCA
Distance-based redundancy analysis (db-RDA)
Empirical example

Thursday 30th
Day 4 09:00 – 12h00
Gradient analysis using siteXspecies data
Simulating environmental gradients
Using simulations to compare ordination methods
The “horseshoe” or “arch” effect
Flexible shortest path adjustments
Recommendations for siteXspecies ordinations
Implications for constrained ordination methods

Course Instructor

Prof. John (Bill) Shipley
Bill Shipley is an experienced researcher and teacher in plant ecology and statistical ecology. He has published four scientific monographs and over 170 peer-reviewed papers.

ONLINE COURSE - Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live
2023-03-13 - 2023-03-17 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, May 12th, 2025

Course Format
Pre Recorded

About This Course
In this three-day course, we introduce species distribution models (SDMs) and ways toincorporate phylogenetic information into single species models using R. We begin byproviding an overview on the use of SDMs as a central tool for ecologists and evolutionarybiologists, review and implement common SDM approaches and introduce hybrid models,which use the information in functional traits to complement the models. We then justifythe rationale for using phylogenetic information in absence of functional trait data andshow how to incorporate phylogenetic information in SDMs (day 1). We review examplesof practical implementation of PSDMs to both present and future climate scenarios (day 2).Finally, we overview more advanced approaches of incorporating phylogenies into models(the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatialcontext (day 3).

Intended Audiences
This course is aimed at anyone who wishes to improve/complement their use of SpeciesDistribution Models using phylogenies.

Course Details
Venue – Delivered remotelyAvailability – 20 placesDuration – 3 daysContact hours – Approx. 18 hoursECT’s – Equal to 2 ECT’sLanguage – English

Teaching Format
The course will be hands-on and workshop based. Throughout each day, there will be some introductory remarks for each new topic, introducing and explaining key concepts.
The course will take place online using Zoom. On each day, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:00pm-16:00pm
All sessions will be video recorded and made available to all attendees.
Attendees in different time zones will be able to join into some of these live broadcasts, even if all of them are not convenient times.
By joining any live sessions that are possible, this will allow attendees to benefit Fromm asking questions and having discussions, rather than just watching prerecorded sessions.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared with the attendees.

Assumed quantative knowledge
We will assume general familiarity with the very basics of statistics (e.g. summarystatistics, distributions).

Assumed computer background
We will assume general familiarity with R elementary operations (e.g. package sourcing,data importing and exporting, object indexing) and some familiarity with programming inR (writing code).

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 12th
Classes from 8:00 to 16:00
Introduction to species distribution models: rationale, algorithms, validation and applications.
• Working with SDMs. Implementing SDMs in R.• Hybrid-SDMs or how to incorporate functional information into the models.• What to do in absence of functional trait data? The rationale for using the latent information in phylogenies instead.• The phylogenetic predictor.

Tuesday 13th
Classes form 08:00 – 16:00
Putting phylogenies in the geography: how to combine phylogenies with speciesdistribution models in R.
• Phylogenetic information can improve both present and future predictions ofspecies distributions.• Projecting phyloSDMs across space and time in R.• When and why phylogenies can and can’t improve models.

Wednesday 14th
Classes form 08:00 – 16:00
Phylogenies also improve models for the temporal distribution of species.

The Bayesian Phylogenetic Mixed Model
Examples of implementation of PMMs and extrapolating their predictions to thegeography in R.

Course Instructor
Dr. Morales Castilla Ignacio

ONLINE COURSE -Introduction to eco-phylogenetics and comparative analyses using R (ECPH02) This course will be delivered live
2023-03-13 - 2023-03-17 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, March 13th, 2023

Course Format
Pre Recorded

About This Course
In this five day course, we provide an introduction to eco-phylogenetics and comparative analyses using R. We begin by providing an overview on the use of phylogenies as a tool for evolutionary biologists and modern techniques to deal with large phylogenies and to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover some of the most relevant eco-phylogenetic analyses and provide examples from the community to themacro-ecological scale (day 2-3). Finally, we introduce a diversity of classic and modern phylogenetic comparative methods to consider the historical relationship of lineages in eco-evolutionary research, including models of trait evolution, analysis of clade diversification and the use of phylogenies in spatial distribution models among others (day 4-5).

Intended Audiences
This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses.

Course Details
Venue – Delivered remotelyAvailability – 30 placesDuration – 5 daysContact hours – Approx. 35 hoursECT’s – Equal to 3 ECT’sLanguage – English

Teaching Format
The course will be hands-on and workshop based. Throughout each day, there will be some introductory remarks for each new topic, introducing and explaining key concepts.
The course will take place online using Zoom. On each day, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:30pm-16:30pm
All sessions will be video recorded and made available to all attendees.
Attendees in different time zones will be able to join into some of these live broadcasts, even if all of them are not convenient times.
By joining any live sessions that are possible, this will allow attendees to benefit Fromm asking questions and having discussions, rather than just watching prerecorded sessions. All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared with the attendees.

Assumed quantative knowledge
We will assume general familiarity with the very basics of statistics (e.g. summary statistics, distributions). As this is an introductory course, no phylogenetic background is required.

Assumed computer background
We will assume general familiarity with R elementary operations (e.g. package sourcing, data importing and exporting, object indexing) and some familiarity with programming in R (writing code).

Equipment and software requirements
Attendees of the course must use a computer with R/RStudio installed, as well as the necessary additional R packages. Instructions on how to install the software will be provided before the start of the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
https://cran.r-project.org/Download RStudio
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 13th
Classes from 8:00 to 16:30
• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists, phylogenetic inference (quick overview), phylogenies aevolutionary hypotheses.
• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning, resolving polytomies, sticking species). Visualizing large phylogenies.
• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom, V.PhyloMaker, SUNPLIN and randtip R package.

Tuesday 14th
• Introduction to the eco-phylogenetic framework, classical conception and posterior modifications.
• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices, null models, applications to biodiversity conservation.
• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity.

Wednesday 15th
• Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses.
• Spatial phylogenetics. RPD, RPE and CANEPE analysis.
• Overview of functional trait ecology. Functional richness, evenness and divergence.Community weighted means.
• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology

Thursday 16th

The phylogenetic comparative method, from independent contrasts to sophisticated modelling.
Analyses of phylogenetic signal and models of evolution: rationale, common- practice, and new trends.
Correlated evolution and ancestral trait reconstruction.
Analyses of diversification, speciation and extinction rates in a geographic context.

Friday 17th

The need to account for phylogenetic relationships in models.
Most common phylogenetic modelling approaches: PGLS, PGLMM, BayesianPMM.
Putting phylogenies in the geography: how to combine phylogenies with species distribution models.

Course Instructor

ONLINE COURSE - Introduction to Multi’omics Data Analysis from Microbial Communities (MOMC01) This course will be delivered live
2023-03-10 | Delivered remotely (USA east), Eastern Daylight Time, MD, United States
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Friday, March 10th, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2

Course Details
The aim of this one-day workshop is to provide a thorough introduction to computational
approaches for the analysis of microbial community profiles with a focus on metagenomic
sequencing data. We will explain how taxonomic and functional profiles are generated from
raw sequencing data, introduce different bioinformatic approaches to process sequencing
data, followed by multivariate statistical analysis and different visualization techniques. The
course will consist of a mixture of lectures and hands-on tutorials. The practical part of the
course will focus on the analysis of publicly available multi-omics profiles.
By the end of the course participants should:
1. Be familiar with different workflows involved in the analysis of large-scale multi-
omics studies.
2. Understand how to generate taxonomic, functional and strain profiles from
metagenomic sequencing data.
3. Be familiar with applying a multivariate statistical framework to generate hypotheses
and account for confounding covariates.
4. Be able to use exploratory data visualizations techniques and visualize results from
the statistical analysis using R.

Intended Audiences
Academics, post-graduate students and researchers working on projects related to microbial community studies, who want to learn computational approaches for the analysis of high-dimensional sequencing data.

Venue
Venue – Delivered remotely

Course Information

Time zone – GMT
Availability – 20 places
Duration – 1 day
Contact hours – Approx. 7 hours
ECT’s – Equal to 1 ECT
Language – English

Teaching Format

The course will be held virtually and consists of a mixture of theoretical and practical
sessions. The concepts and tools will be first described and explained, followed by a lab
session with hands-on experience of applying the tool to provided data sets. At the end of
the day there will be additional time for questions.

Assumed quantitative knowledge
Attendees are assumed to have a basic understanding of microbial community studies. The
course will not cover data generation aspects (sample collection, library preparation etc) but
focus on how to analyse sequencing data and taxonomic/functional microbial community
profiles.

Assumed computer background
Familiarity with the command line interface (bash/shell) and R is an advantage. We will offer
short introductory labs for both to make the course more accessible to a wider audience.
We also encourage attendees to get familiar with zoom prior to the course.

Equipment and software requirements
Attendees are expected to have their own laptop with a microphone and ideally a camera.
We encourage all attendees to keep their camera on during the lectures and tutorials. Zoom
should be installed prior to the course. For the tutorials will use R Studio Cloud which you
can access through your browser. The setup instructions for R Studio Cloud will be sent prior
to the course start.
RStudio – https://www.rstudio.com/products/rstudio/download/

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
oliverhooker@prstatistics.com

COURSE PROGRAMME

Friday 10th – Classes from 09:00 to 18:00
Lecture 1 – general Introduction
Lecture 2 – Introduction to microbial community analysis
Practical 1 – Introduction to R and R notebook
Lecture 3 – Metagenomic data visualisation and exploratory analysis with cMD
Practical 2 – Metagenomic data visualisation
Lecture 4 – Statistics for microbial multi-comics data, methods for multi-comics integration
Practical 3 – Multivariate analysis (Linear models and/or MaAsLin2
Lecture 5 – Large-scale multi-omics studies
Conclusions – Discussion, questions, wrap up!

Course Instructor

Dr. Melanie Schirmer
Melanie is a computational biologist studying the human microbiome and its role in health and disease as an Emmy Noether Group leader at the Technical University in Munich, Germany. In many diseases, such as chronic inflammatory bowel diseases and immune-related diseases, an imbalance of the microbial communities, that live in and on our bodies, has been observed. The underlying reasons and consequences of this imbalance are largely unknown though. Previous studies have identified taxonomic changes of the microbiome and disease-associated bacterial species. However, different strains of the same species can substantially differ in their functional capacities. Therefore, it is crucial to investigate functional and metabolic differences of microbial strains, in order to develop effective therapies and strategies to prevent these diseases. We are addressing these questions with computational analyses of multi-omics data in combination with experimental validation of the immunogenicity and inflammatory activity of the identified strains. Our research provides insights into the potential mechanisms of the human microbiome in autoimmune and inflammatory diseases.
Works at – Technical University of Munich
Teaches – Introduction to Multi’omics Data Analysis of Microbial Communities (MOMC)

ONLINE COURSE - Making Beautiful And Effective Maps In R (MAPR04) This course will delivered live
2023-02-23 - 2023-02-24 | Delivered remotely (Poland), Central European Summer Time, Poland
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Thursday, February 23rd, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
The aim of the course is to show you how to use R to make pretty, yet appealing maps using the R programming language. Several R packages related to spatial data processing and visualization will be introduced during the course. The course will teach you how create publication-ready static maps, animated maps, interactive maps, and simple map applications using a mixture of lectures and computer exercises.
By the end of the course participants should:

Understand the basic concepts behind the tmap package
Be able to create a variety types of static maps, including raster maps, choropleth maps, and point maps
Know how to create interactive maps and simple map applications using the shiny package
Be able to create facet maps and map animations to represent spatiotemporal phenomenon
Know how to utilize specific-purpose mapping packages to create cartograms or grid maps
Have the confidence to apply map making skills to their own projects

Intended Audiences
Academics and post-graduate students working on projects related to spatial data and want to create publication-ready maps, interactive maps for their websites, or simple mapping web applications
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R to quickly create maps for their reports or websites
The course is designed for intermediate R users interested in maps making and R beginners who have prior experience with geographic data.

Venue
Delivered remotely

Course Details
Availability – 20 places
Duration – 2 days
Contact hours – Approx. 16 hours
ECT’s – Equal to 1.5 ECT’s
Language – English

Teaching Format
The course will be a mixture of theoretical and practical. Each concept will be first described and explained, and next the attendees will exercise the topics using provided data sets.

Assumed quantitative knowledge
Understanding basic GIS concepts, such as spatial vector, spatial raster, coordinate reference systems would be beneficial, but is not necessary.

Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.
However, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types, and some programming experience, the course may also be appropriate for you

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Thursday 24th
Classes from 09:00 to 17:00Introduction to mapping packages in RMaking static mapsApplying point, lines, polygons, and raster map layersCustomizing mapsCreating interactive mapsSaving maps

Friday 25th
Classes from 09:00 to 17:00Making facet mapsCreating animated mapsMaking inset mapsUsing specific-purpose mapping packagesCreating simple map applicationsOther mapping packages in R

Jakub Nowosad
Works at: Adam Mickiewicz University
Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create, collaborate, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book.
ResearchGateGoogleScholarORCIDLinkedInGitHub

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

ONLINE COURSE - Introduction to Aquatic Acoustic Telemetry (IAAT02) This course will be delivered live
2023-01-30 - 2023-02-03 | Delivered remotely (Canada)
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, January 30th, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UK Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
Acoustic telemetry is a popular method for monitoring the movements and behaviour of aquatic animals globally. Increasingly smaller tags along with improvements in battery technology have allowed for tagging a wide varietyof species and life stages, enabling monitoring of individuals as small as salmon smolts and as large as whale sharks for periods from 30 days to 10 years. In addition, with more and more tag sensor and environmentalmonitoring options available, telemetry datasets are becoming richer, allowing researchers to answer increasingly complex questions about why animals move where and when they do. Receiver technology also continues to evolve and increasingly allows for data to be collected at finer spatial and temporal scales than ever before. New technologies such as gliders and real-time detection systems allow broad geographic coverage and remote, real-time access to animal movement data. Additional advancements in built-in acoustic array diagnostics permit increasingly detailed analyses of system performance over time, resulting in more robust interpretation of animalmovement data.
In this course you will learn about the different types of Innovasea acoustic telemetry technologies and their applicability for use in different study environments and in answering different research questions. Exampleapplications that will be discussed include: monitoring fine-scale movements and behaviour around barriers and other structures, migration survival studies around barriers, monitoring spawning and other seasonal behaviours,real-time monitoring, home range and Marine Protected Area studies, habitat selection, species interactions, and investigating causes of mortality. This section will include a deep dive into the logistics of fine-scale positioningstudies and will provide an overview of the different types of analyses that are commonly performed with positional data.
A robust telemetry study design that accounts for the advantages and limitations of different equipment options is critical to ensure a successful study. During this course, you will learn about important study design considerations such as appropriate hardware models and tag programming parameters for your study objectives, tag attachment considerations, and how to optimize your receiver placements through early and thorough testing. We will analyze an example range test dataset and discuss the implications of range test results on array design. We will also review considerations when designing moorings for your particular study environment.
To ensure successful execution of your telemetry study plan and the highest quality data, it’s important to use telemetry best practices when preparing for and during tagging and deployment. We will review best practices for acoustic telemetry equipment maintenance and care, pre-deployment testing, tips for preparing equipment and data logs, and how to test your array once deployed in the field. We will also look at how to monitor the performance of your telemetry array throughout the duration of your study, reviewing what metrics are best used to determine whether the array is operating as planned so you can have confidence in the data being collected. Finally, because interpretation of acoustic telemetry data and inferring animal behaviour from these data is often confounded by array performance questions, this course will teach you techniques for assessing system performance to aid in the correct interpretation of animal detection data. Finally, since telemetry datasets are growing larger all of the time, data management is becoming increasingly challenging. During this course you will learn about data management best practices and tools to perform basic quality assurance, basic visualizations, and basic filtering of large datasets in preparation for statistical analyses. You will also have the opportunity to discuss your own telemetry studies with the experts during a Q&A sessionon the final day of the course. Bring your data and your questions!

Intended Audiences
This course is suitable for research postgraduates, practicing academics, or persons in industry or government who are working with acoustic telemetry, or planning an acoustic telemetry study, to monitor aquatic animal movement and behaviour. This course focuses on applications of the technology and best practices for obtaining meaningful animal movement data using acoustic telemetry.

Venue
Delivered remotely

Course Information
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 16 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course offers 16 hours of acoustic telemetry content over 4 days. Each day consists of 2 hours lecture, 1 hour break, and another 2 hours lecture. Lecture days are Monday, Tuesday, Thursday and Friday. Friday’s lecture is shorter with remaining time dedicated to interactive participant Q&A and study design or data review. The course will take place online.

Assumed quantitative knowledge
We will assume that you are familiar with basic statistical concepts, linear models, and statistical tests, however statistics knowledge is not required to benefit from this course.

Assumed computer background
Familiarity with data manipulation in Microsoft Excel and ability to import/export data into a data management / statistical package of your choice.

Equipment and software requirements
Computer work is not required during this course however it will be possible for participants to follow along with software demonstrations during the lectures. Software download links will be provided prior to the course. Desktop software applications require a modern Windows operating system.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 30th – Classes from 12:30 to 17:30
IntroductionTypes of Telemetry TechnologyHow Does Acoustic Telemetry Work?
Fine-Scale Positioning Deep Dive
Acoustic Telemetry Applications – Part IExample case studies include: fine-scale monitoring around barriers, migration survival studies, spawning behaviour, real-time monitoring, home range and MPA use, habitat selection, species interactions, and investigation into causes of mortality.
Tuesday 31st – Classes from 12:30 to 17:30
Acoustic Telemetry Applications – Part IIExample case studies include: fine-scale monitoring around barriers, migration survival studies, spawning behaviour, real-time monitoring, home range and MPA use, habitat selection, species interactions, and investigation into causes of mortality
Designing a Successful Acoustic Telemetry StudyDefining the Question and Data NeedsHardware SelectionTag ProgrammingTag Attachment MethodRange TestingReceiver PlacementReceiver Mooring Design
Thursday 2nd – Classes from 12:30 to 17:30
Running Your Acoustic Telemetry StudyEquipment Maintenance & CarePre-Deployment TestingPreparing for Tagging & DeploymentTesting Your Deployment
System Performance AssessmentIn the FieldPost-Study Performance Assessment
Friday 3rd – Classes from 12:30 to 17:30
 Data Management Data Quality Assurance Acoustic Telemetry Q & A

Course Instructor
Stephanie Smedbol
Director of Product Management & Customer Success, Innovasea Fish TrackingStephanie has been working with Innovasea for 12 years in a number of roles in R&D, Product, Management, and Customer Success. Her primary areas of focus are ensuring that researchers get the most out of their telemetry equipment and data, and improving telemetry products and services to enable new and better science. Stephanie’s areas of expertise include telemetry study design, telemetry field work, telemetry system performance analysis, technical training, and technical problem solving.​ Stephanie has a Bachelor ofScience in Biology from McGill University (Montreal, Canada) and a Bachelor of Engineering (Electrical) from Dalhousie University in Nova Scotia, Canada.​
Colleen Burliuk
Research Biologist, Innovasea Fish Tracking
Courtney MacSween
Customer Engagement Coordinator, Innovasea Fish Tracking

ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live
2023-01-16 - 2023-03-06 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, January 16th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
This course introduces the participants to the main concepts and methods of trait-based ecology. While traits have been used in ecology for a long time, an approach explicitly based on traits has been increasingly introduced to almost all aspects of ecological research in the last two decades. In particular, since the early 2000s, methodological developments have really flourished, up to a point that it is hard to keep track of such developments. In this course, we will combine lectures providing an overview of the main principles and methods of trait-based ecology with practices using the statistical software R, so that participants will acquire a knowledge of available R packages and customized functions, and how to use them in the context of trait-based analyses. The course will span methods taking both species-level and community-level perspectives that can be applied to a large variety of organisms. Additional practical aspects that will be covered include the choice of the “right” traits for a given study, what to consider when using trait data from data bases, and how to design and optimize your own trait sampling campaign. The course is largely based on the book recently published by Cambridge University Press “Handbook of Trait-Based Ecology: From Theory to R Tools” and the accompanying R material. The book is not required for course participation.

Intended Audiences
Master and PhD students, as well as post docs and established researchers new to the topic, who are at the start of their own trajectory in trait-based ecological research.

Venue
Delivered remotely

Course Details
Time zone – GMTAvailability – 30 places
Duration – 8 days (4 hours per day, one day per week, for 8 weeks)
Contact hours – Approx. 32 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
The course will consist in 1 teaching block per week, for 8 weeks. Each block will consist of approximately 3.5 hours of interactive live online sessions (at xx:xx GMT time), which will include theoretical lectures, discussion, and demonstrations of R code of selected packages and functions and approximately 4 hours of practical’s that each participant will do on their own schedule / time zone, based on annotated self-explanatory R scripts. The instructor will be available for questions and help during Western European working hours and a bit beyond that, depending on the participants’ time zones. Data sets and R codes for practicals will be provided, so that participants can repeat and extend the methods demonstrated during the lectures, at their own convenience.

Assumed quantitative knowledge
A basic knowledge of uni- and multivariate statistical analyses is assumed (correlation, simple regression models, unconstrained and constrained ordination, e.g. PCA, RDA). Without such knowledge the course can probably be followed for most parts, but the practicals will be much less efficient for the student.

Assumed computer background
Participants should have basic experience in working with the R statistical environment, preferably in connection with the R studio interface. They should be familiar with importing data to R, installing and loading packages, and basic plot functions.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 16th January
Classes from 10:30 to 14:30
Instructor Francesco Bello
Theory
Introduction, definitions, response and effect, functional groups, trade-offs, Gower distance.
Practical
People’s trait game
Gower distance

Monday 23rd January
Classes from 10:30 to 14:30
Instructor Francesco Bello
Theory
Community Weighted Mean (CWM) and Functional Diversity (FD)
Practical
CWM & FD

Monday 30th January
Classes from 10:30 to 14:30
Instructor Lars Götzenberger
Theory
Response traits and environmental filtering
Practical
Kleyer appendix

Monday 6th February
Classes from 10:30 to 14:30
Instructor Carlos Pérez Carmona
Theory
Community assembly
Practical
Basics of null-models

Monday 13th February
Classes from 10:30 to 14:30
Instructor Matty Berg & Carlos Pérez Carmona
Theory
Intraspecific trait variability
Practical
Trait overlap (trova), Trait variance, CWM flex anova

Monday 20th February
Classes from 10:30 to 14:30
Instructor Lars Götzenberger
Theory
Phylogeny
Practical
Conservatism, Phylogenetic diversity, PICs

Monday 27th February
Classes from 10:30 to 14:30
Instructor Marco Moretti & Francesco Bello
Theory
Response & Effect traits
Practical
Selection/Complementarity, Lautaret, multitrophic

Monday 6th March
Classes from 10:30 to 14:30
Instructor Lars Götzenberger & Carlos Pérez Carmona
Theory
Missing traits, databases, sampling traits
Practical
Databases extraction, sampling game, data imputation

ONLINE COURSE - Ecological niche modelling using R (ENMR04) This course will be delivered live
2022-12-12 - 2022-12-16 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, December 12th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course

The course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course, attendees will have the capacity to perform ecological niche models and understand their results, as well as to choose and apply the correct methodology depending on the aim of their type of study and data.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for similar mechanistic or correlative models, empirical or mathematical approaches to the ecological niche of a species, where different types of ecogeographical variables (environmental, topographical, human) are related with a species physiological data or geographical locations, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular due to the need for efficiency in the design and implementation of conservation management.
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent, Bioclim, Domain, and logistic regressions, and R packages for computing ENMs like Dismo and Biomod2. Also, students will learn to compare different ecological niche models using the Ecospat package.

Intended Audiences

This course is orientated to PhD and MSc students, as well as persons in researcher or industry working on biogeography, spatial ecology, or related disciplines.

Venue
Delivered remotely

Course Details
Time Zone – Western European Time
Availability – 24 Places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and applications of ENM. Practical lectures on most used ENM methods. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructor, but participants are welcome to bring their own data.

Assumed quantitative knowledge
Basic knowledge in Geographical Information Systems and spatial analyses.

Assumed computer background
Familiarity with GIS software like QGIS. Ability to visualise shapefiles and raster files. Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 12th
Classes from 09:30 to 17:30
Elementary concepts on Ecological Niche Modelling
Module 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory, types of ecological niches, types of ENM, diagram BAM, ENMs as approximations to species’ niches.
Module 2: Problems and limitations on ENM. Assumptions and uncertainties, equilibrium concept, niche conservatism, autocorrelation and intensity, sample size, correlation of environmental variables, size and form of study area, thresholds, model validation, model projections.
Module 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis, Biomod, Domain, Habitat, Distance of Mahalanobis, ENFA, GARP, Maxent, Logistic regression, Generalised Linear Models, Generalised Additive Models, Generalised Boosted Regression Models, Random Forest, Support Vector Machines, Artificial Neural Network.
Module 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step.
Module 5: Applications of ENM. Ecological niche identification, Identification of contact zones, Integration with genetical data, Species expansions, Species invasions, Dispersion hypotheses, Species conservation status, Prediction of future conservation problems, Projection to future and past climate change scenarios, Modelling past species, Modelling species richness, Road-kills, Diseases, Windmills, Location of protected areas.

Tuesday 13th
Classes from 09:30 to 17:30
Prepare environmental variables and run ecological niche models with dismo package.
Module 6: Preparing variables. Choosing environmental data sources, Downloading variables, Clipping variables, Aggregating variables, Checking pixel size, Checking raster limits, Checking NoData, Correlating variables.
Module 7: Dismo practice. How to run an ENM using the R package dismo.

Wednesday 14th
Classes from 09:30 to 17:30
Run ecological niche models with Biomod2 package and Maxent.
Module 8: Biomod2 practice. How to run an ENM using the R package Biomod2.
Module 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software.

Thursday 15th
Classes from 09:30 to 17:30
Compare ecological niche models with ecospat.
Module 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat.
Module 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM.

Friday 16th
Classes from 09:30 to 17:30
Run ecological niche models with your own data.
Module 12: Final practical. In this practical, the students will run ENM with their own data or with a new dataset, applying all the methods showed during the previous days.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal websiteWork WebpageResearchGateGoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Ecological niche modelling using R (ENMRPR)
2022-12-12 - 2022-12-15 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Course Format
Pre Recorded

About This Course

The course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course, attendees will have the capacity to perform ecological niche models and understand their results, as well as to choose and apply the correct methodology depending on the aim of their type of study and data.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for similar mechanistic or correlative models, empirical or mathematical approaches to the ecological niche of a species, where different types of ecogeographical variables (environmental, topographical, human) are related with a species physiological data or geographical locations, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular due to the need for efficiency in the design and implementation of conservation management.
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent, Bioclim, Domain, and logistic regressions, and R packages for computing ENMs like Dismo and Biomod2. Also, students will learn to compare different ecological niche models using the Ecospat package.

Intended Audiences

This course is orientated to PhD and MSc students, as well as persons in researcher or industry working on biogeography, spatial ecology, or related disciplines.

Course Details
Last Up-Dated 15:03:2019
Duration – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and applications of ENM. Practical lectures on most used ENM methods. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructor, but participants are welcome to bring their own data.

Assumed quantitative knowledge
Basic knowledge in Geographical Information Systems and spatial analyses.

Assumed computer background
Familiarity with GIS software like QGIS. Ability to visualise shapefiles and raster files. Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1
Day 1 – Approx. 7 hours
Elementary concepts on Ecological Niche Modelling
Module 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory, types of ecological niches, types of ENM, diagram BAM, ENMs as approximations to species’ niches.
Module 2: Problems and limitations on ENM. Assumptions and uncertainties, equilibrium concept, niche conservatism, autocorrelation and intensity, sample size, correlation of environmental variables, size and form of study area, thresholds, model validation, model projections.
Module 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis, Biomod, Domain, Habitat, Distance of Mahalanobis, ENFA, GARP, Maxent, Logistic regression, Generalised Linear Models, Generalised Additive Models, Generalised Boosted Regression Models, Random Forest, Support Vector Machines, Artificial Neural Network.
Module 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step.
Module 5: Applications of ENM. Ecological niche identification, Identification of contact zones, Integration with genetical data, Species expansions, Species invasions, Dispersion hypotheses, Species conservation status, Prediction of future conservation problems, Projection to future and past climate change scenarios, Modelling past species, Modelling species richness, Road-kills, Diseases, Windmills, Location of protected areas.

Day 2
Day 2 – Approx. 7 hours
Prepare environmental variables and run ecological niche models with dismo package.
Module 6: Preparing variables. Choosing environmental data sources, Downloading variables, Clipping variables, Aggregating variables, Checking pixel size, Checking raster limits, Checking NoData, Correlating variables.
Module 7: Dismo practice. How to run an ENM using the R package dismo.

Day 3
Day 3 – Approx. 7 hours
Run ecological niche models with Biomod2 package and Maxent.
Module 8: Biomod2 practice. How to run an ENM using the R package Biomod2.
Module 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software.

Day 4
Day 4 – Approx. 7 hours
Compare ecological niche models with ecospat.
Module 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat.
Module 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM.

Day 5
Day 5 – Approx. 7 hours
Run ecological niche models with your own data.
Module 12: Final practical. In this practical, the students will run ENM with their own data or with a new dataset, applying all the methods showed during the previous days.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal websiteWork WebpageResearchGateGoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

ONLINE COURSE – Time Series Data Analysis (TSDA02) This course will be delivered live
2022-11-15 - 2022-11-18 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, November 16th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
This course covers introductory modelling for the analysis of time series data. The main focus of the course is on data observed at regular (discrete) time points but later modules cover continuously-observed data. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular forecast package. The second half of the course looks at Bayesian time series analysis which is extremely customisable to bespoke data analysis situations.

Intended Audiences

Research postgraduates, practicing academics, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data.

Venue
Delivered remotely

Course Details
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format

The course will be divided into theoretical lectures to introduce and explain key concepts and theories. Afternoon practicals will be based on the topics covered in the morning lectures.

Assumed quantitative knowledge
A basic understanding of regression methods and generalised linear models.
Some familiarity with R including the ability to import/export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.

Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.

Equipment and software requirements
EQUIPMENT AND SOFTWARE REQUIREMENTS

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

Course Programme

Wedesday 16th

9:30-10:30
Introduction, example data sets

10:30-10:45
Coffee break

10:45-11:45
Revision: likelihood and inference

11:45-12:00
Break

12:00-13:00
Revision: linear regression and GLMs

13:00-14:00
Lunch

14:00-14:45
Tutor-guided practical: Loading data in R and running simple analysis

14:45-15:00
Coffee break

15:00-17:00
Self-guided practical: Using R for linear regression and GLMs’

Thursday 17th

9:30-10:30
Auto-regressive models and random walks

10:30-10:45
Coffee break

10:45-11:45
Moving averages and ARMA

11:45-12:00
Break

12:00-13:00
Integrated models and ARIMA

13:00-15:00
Lunch

15:00-15:45
Tutor-guided practical: the forecast package in R

15:45-16:00
Coffee break

16:00-17:00
Self-guided practical: Fitting ARIMA models with forecast

Friday 18th

9:30-10:30
Including covariates: ARIMAX models

10:30-10:45
Coffee break

10:45-11:45
Creating bespoke time series models using Bayes

11:45-12:00
Break

12:00-13:00
Model choice and forecasting using Bayes

13:00-14:00
Lunch

14:00-14:45
Tutor-guided practical: a walkthrough example time series analysis

14:45-15:00
Coffee break

15:00-17:00
Self-guided practical: finding the best time series model for your data set

Tuesday 22nd

9:30-10:30
Modelling with seasonality and the frequency domain (slides)

10:30-10:45
Coffee break

10:45-11:45
Stochastic volatility models and heteroskedasticity (slides)

11:45-12:00
Break

12:00-13:00
Fitting Bayesian time series models (slides)

13:00-14:00
Lunch

14:00-14:45
Tutor-guided practical: fitting time series models in JAGS and Stan (code)

14:45-15:00
Coffee break

15:00-17:00
Self-guided practical: start analysing your own data set with Bayes (worksheet)

ONLINE COURSE - Bayesian Data Analysis (BADA02) This course will be delivered live
2022-10-18 - 2022-10-20 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Tuesday, 18th October, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses, many researchers do not have opportunities to learn the fundamentals of Bayesian methods, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods, both theoretically and practically. We will begin by teaching the fundamental concepts of Bayesian inference and Bayesian modelling, including how Bayesian methods differ from their classical statistics counterparts, and show how to do Bayesian data analysis in practice in R. We then provide a solid introduction to Bayesian approaches to these topics using R and the brms package. We begin by covering Bayesian approaches to linear regression. We will then proceed to Bayesian approaches to generalized linear models, including binary logistic regression, ordinal logistic regression, Poisson regression, zero-inflated models, etc. Finally, we will cover Bayesian approaches to multilevel and mixed effects models. Throughout this course, we will be using, via the brms package, Stan based Markov Chain Monte Carlo (MCMC) methods.

Intended Audiences
This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science, including the social sciences, life sciences, physical sciences. No prior experience or familiarity with Bayesian statistics is required.

Venue
Delivered remotely

Course Information
Availability – 30 places
Duration – 3 days
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at:• 10am-12pm• 1pm-3pm• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub

Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 18th
Classes from 10:00 to 18:00
• Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach, which is referred to variously as the classical, or sampling theory based, or frequentist based approach, rather than being a specialized or advanced statistics topic. However, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition, and a pragmatic blend of both approaches is entirely possible.
• Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such, it can be used as a means for performing statistical inference. In this section of the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an application of these methods to more complex statistical models, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases.
• Topic 3: Bayesian inference in a simple statistical model. In this section, we will work through a classic statistical inference problem, namely inferring the number of red marbles in an urn of red and black marbles, or equivalent problems. This problem is easy to analyse completely with just the use of R, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function, prior distributions, posterior distributions, maximum a posteriori estimation, high posterior density intervals, posterior predictive intervals, marginal likelihoods, Bayes factors, model evaluation of out-of-sample generalization.

Wednesday 19th
Classes from 10:00 to 18:00
• Topic 4: Bayesian analysis of normal models. Statistical models based on linear and normal distribution are a mainstay of statistical analyses in general. They encompass models such as linear regression, Pearson’s correlation, t-tests, ANOVA, ANCOVA, and so on. In this section, we will describe how to do Bayesian analysis of normal linear models, focusing on simple examples. One of the aims of this section is to identify some important and interesting parallels between Bayesian and classical or frequentist analyses. This shows how Bayesian and classical analyses can be seen as ultimately providing two different perspectives on the same problem.
• Topic 5: The previous section provides a so-called analytical approach to linear and normal models. This is where we can calculate desired quantities and distributions by way of simple formulae. However, analytical approaches to Bayesian analyses are only possible in a relatively restricted set of cases. On the other hand, numerical methods, specifically Markov Chain Monte Carlo (MCMC) methods can be applied to virtually any Bayesian model. In this section, we will re-perform the analysis presented in the previous section but using MCMC methods. For this, we will use the brms package in R that provides an exceptionally easy to use interface to Stan.
Topic 6: Bayesian linear models. We begin by covering Bayesian linear regression. For this, we will use the brm command from the brms package, and we will compare and contrast the results with the standard lm command. By comparing and contrasting brm with lm we will see all the major similarities and differences between the Bayesian and classical approach to linear regression. We will, for example, see how Bayesian inference and model comparison works in practice and how it differs conceptually and practically from inference and model comparison in classical regression. As part of this coverage of linear models, we will also use categorical predictor variables and explore varying intercept and varying slope linear models.

Thursday 20th
Classes from 10:00 to 18:00
• Topic 7: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that do not always hold in practice. For example, they assume a normal distribution of the residuals, and assume homogeneity of variance of this distribution across all values of the predictors. In Bayesian models, these assumptions are easily relaxed. For example, we will see how we can easily replace the normal distribution of the residuals with a t-distribution, which will allow for a regression model that is robust to outliers. Likewise, we can model the variance of the residuals as being dependent on values of predictor variables.
• Topic 8: Bayesian generalized linear models. Generalized linear models include models such as logistic regression, including multinomial and ordinal logistic regression, Poisson regression, negative binomial regression, zero-inflated models, and other models. Again, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. In our coverage of this topic, we will see how powerful Bayesian methods are, allowing us to easily extend our models in different ways in order to handle a variety of problems and to use assumptions that are most appropriate for the data being modelled.
• Topic 9: Multilevel and mixed models. In this section, we will cover the multilevel and mixed effects variants of the regression models, i.e. linear, logistic, Poisson etc, that we have covered so far. In general, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups, and so on). For this, we use a wide range of real-world data-sets and problems, and move between linear, logistic, etc., models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models, and how to choose between maximal and minimal models. We will also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical problems (e.g. lack of model convergence) that beset classical approaches.

Dr. Mark Andrews
Works at: Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches:

Free Introduction To Statistics Using R And Rstudio (IRRS)
Introduction To Generalised Linear Models Using R And Rstudio (IGLM)
Introduction To Mixed Models Using R And Rstudio (Immr)
Nonlinear Regression Using Generalized Additive Models Using R (GAMR)
Introduction To Machine Learning And Deep Learning Using R (IMDL)
Model Selection And Model Simplification (MSMS)
Data Visualization Using Gg Plot 2 (R And Rstudio) (DVGG)
Data Wrangling Using R And Rstudio (DWRS)
Reproducible Data Science Using Rmarkdown, Git, R Packages, Docker, Make & Drake, And Other Tools (RDRP)
Introduction/Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDA)
Bayesian Data Analysis (BADA)
Bayesian Approaches To Regression And Mixed Effects Models Using R And Brms (BARM)
Introduction To Stan For Bayesian Data Analysis (ISBD)
Introduction To Python (PYIN)
Introduction To Scientific, Numerical, And Data Analysis Programming In Python (PYSC)
Machine Learning And Deep Learning Using Python (PYML)
Python For Data Science, Machine Learning, And Scientific Computing (PDMS)

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
ResearchGate

ONLINE COURSE - Bioacoustics For Ecologists: Hardware, Survey design And Data analysis (BIAC03) This course will be delivered live
2022-10-03 - 2022-10-05 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Tuesday, September 20th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
Time Zone
TIME ZONE – GMT – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About this course
This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data.
By the end of this 5-day practical course, attendees will have the capacity to set up and deploy recording devices, download acoustic data, how to analyse this data and report the results.
Bioacoustic methods are becoming increasingly recognised as a valuable approach for ecological surveying. Bioacoustics can be used to effectively replace some current techniques whilst increasing the quality of the data collected or can be used in unison to compliment them. They are particularly useful for developing long-term, permanent datasets that can be independently reviewed, particularly for rare species with low detectability, or when working in difficult environments.
The course will provide a practical introduction to bioacoustics methods, with a mix of lectures and practical workshops, and some optional fieldwork. It will start with a basic introduction to sound and recording theory, before developing hands-on skills in setting-up and deploying a range of acoustic and ultrasonic audio recorders. Workshops will then cover the download and analysis of audio data, mainly using Kaleidoscope Pro and Audacity software. The processed audio data will then be analysed and presented using R, the free software environment for statistical computing and graphics (http://www.r-project.org/).
Example data sets will mostly cover applications for bat and bird surveys, as well as the use of Acoustic Indices as biodiversity metrics. If you are working in different areas of ecology using bioacoustics please feel free to contact oliverhooker@prstatistics.com so we can advise if the learning outcomes are transferable to your field of research.

Intended Audiences
This course is suitable for anyone working with bioacoustics from those in academia, conservation biologists and persons in industry and government.

Venue
Delivered remotely

Course Details
Time Zone – GMT
Availability – 15 places
Duration – 3 days
Contact hours – Approx. 21 hours
ECT’s – Equal to 1.5 ECT’s
Language – English

Teaching Format
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

Course Programme

Tuesday 20th
Classes from 09:30 – 17:30
SESSION 1 – INTRO TO ACOUSTIC DATA (AND METADATA)
1. Acoustic Data and Metadata – what does it look like?Data sources – survey methods/approaches, recorder hardware, file types etcMetadata recording and systemsCase study examples – terrestrial & freshwater (& marine)
2. Introduction to spectrogramsVisualizing sound – understanding spectrograms, identifying speciesBats – peakfreq, IPI, max, min, duration, shape etc..Birds – Nathan Pieplow keys – time/frequency characteristics, song/call shapesMeasuring parameters manually and programatically
3. Introduction to audio software – for species ID and vocalizationsAnalysis tools for acoustic dataSoftware tools – Kaleidoscope, Audacity, R (others: Raven/Lite, Batscan, Batsound, Batscope, iBatsID, Analook, SonoChiro, Sonobat, Luscinia, BirdNet, MATLAB, PAMGUARD, etc)Viewing/listening/measuring, recognizers, clusteringManual and automated call detection and ID methodsLimitations and emerging opportunities in acoustic data analysis
4. Workshop – sound editing, measuring and management using Audacity
SESSION 2 – ANALYSING BAT DATA USING KALEIDOSCOPE
5. Workshop – Kaleidoscope bat ID processing (Paul H-L)

Wednesday 21st
Classes from 09:30 – 17:30
SESSION 3 – ANALYSING ACOUSTIC DATA USING R)
6. Workshop – R (Seewave/Soundecology) (creat/view/analyse spectrograms)
SESSION 4 – INTERPRETING ACOUSTIC DATA
7. Data collation, analysis and interpretationMoving from sound to data to meaning (creating tidy data/metadata and using this)Data and recognizer quality – false positives/negatives and validating auto-IDs…Presence/absenceActivity levelsDistributionTemporal changesPopulation assessments/occupancyLocalizing calls with amplitude levels or microphone arraysIdentifying individualsMention of Soundscapes and Acoustic indices – more on this later
8. Soundscapes and Acoustic indicesWhat different indicesPros and cons of eachUsing and comparing scores
9. Example workflows from previous studiesCarlos capercaillie and TBH workBCT/CIEEM guidance on call assessmentOther published research and recommendations

Thursday 22nd
Classes from 09:30 – 17:30
SESSION 5 –ACOUSTIC INDICES USING R/KALEIDOSCOPE
10. Workshop – Kaleidoscope (analyse Acoustic Indices)
11. Workshop – R (Seewave/Soundecology) (analyse Acoustic Indices)
SESSION 6 –SPATIAL ACOUSTIC DATA AND COURSE ROUND-UP
12. Workshop – presenting spatial data using Google Earth and REMtouch kml output – Google EarthCSV outputSpatial analysis with R
13. Review and roundup/conclusions

Course Instructor

*
Dr. Carlos Abrahams
Works at – Technical Director at Baker Consultants Ltd and Senior Lecturer at Nottingham Trent UniversityTeaches – Bioacoustics for ecologists: Hardware, Survey design and Data analysis (BIAC)
Carlos has been working in the practical fields of ecology and nature conservation for over 25 years. Starting his career in nature reserve and countryside management, he has been an ecological consultant since 2001. Alongside managing a busy consultancy, undertaking Environmental Impact Assessments for a range of clients, he is also a part-time lecturer at Nottingham Trent University on the BSc Environmental Biology. Carlos has previously published research on wetland vegetation/management and amphibian habitat selection. However, after many years of using static and handheld detectors for bat surveys, he is currently engaged in studying the potential of bioacoustic methods for investigating bird populations, especially for rare and declining species such as Capercaillie and Nightjar.

FREE SEMINAR - Remote Sensing With Satellite Multi-Spectral Sensors, drone RGB and Near Infrared cameras and Aircraft And Drone LiDAR Sensors (RSFS01)
2022-07-13 16:00 - 16:30 (Europe/London) | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, July 20th, 2022

Free seminar

This is a free ~30 minute seminar including a Q and A session at the end for our up-coming courses, Remote Sensing with satellite multi-spectral sensors (RSMS01), Remote Sensing with drone RGB and Near Infrared cameras (RSWD01) and Remote Sensing with aircraft and drone LiDAR sensors (RSLD01).

Time
16:00 – 16:30 GMT

Speaker

Course Instructor Nelson Pires
About these courses

Satellite Remote Sensing has become a common tool to investigate the different fields of Earth and environmental sciences. The progress of the performance capabilities of the optoelectronic and radar devices mounted on-board remote sensing platforms have further improved the capability of instruments to acquire information about the Earth and its resources for global, regional and local assessments. Disciplines such as agriculture, hydrology, and ecosystem studies have all developed a strong Remote Sensing component, facilitating our understanding of the environment and its processes over a broad range of spatial and temporal scales.This 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications, covering issues related to Remote Sensing, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of Earth Observation data.

Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential Remote Sensing platform that could satisfy the needs of a wide range of civilian applications. The continuous developments in direct georeferencing and Remote Sensing (i.e., passive and active imaging sensors in the visible and infrared range – RGB cameras and LiDAR) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information used in environmental research to collect information about the Earth, such as vegetation and tree species. This 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications, covering issues related to LiDAR sensors coupled on aircraft and UAVs, computing exercises on the processing of 3D point clouds to produce geospatial products.

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

Course Instructor

Dr. Nelson Pires
Works at –
Teaches –

ONLINE COURSE - Introduction to spatial analysis of ecological data using R (ISPE05) This course will delivered live
2022-07-11 - 2022-07-14 | Delivered remotely (Poland), Central European Summer Time, Poland
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, July 11th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About this course
The aim of the course is to introduce you to a spatial data processing, analysis, and visualization capabilities of the R programming language. It will teach a range of techniques using a mixture of lectures, computer exercises and case studies.
By the end of the course participants should:

Understand the basic concepts of spatial data analysis
Know R’s spatial capabilities
Understand how to import a range of spatial data sources into R
Be confident with using R’s command-line interface (CLI) for spatial data processing
Be able to perform a range of attribute operations (e.g. subsetting and joining), spatial operations (e.g. distance relations, topological relations), and geometry operations (e.g. clipping, aggregations)
Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
Know how to visualize the results of a spatial analysis in the form of static and interactive maps
Have the confidence to apply spatial analysis skills to their own projects

Intended Audiences
Academics and post-graduate students working on projects related to spatial data and want access to a powerful (geo)statistical and visualization programming language.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data.

Venue
Delivered remotely

Course Details
Venue – Delivered remotely
Time zone – Poland local time (UTC+2)
Availability – 20 places
Duration – 4 days
Contact hours – Approx. 27 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format

The course will be a mixture of theoretical and practical. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.

Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data.

Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.
However, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types, and some programming experience, the course may also be appropriate for you.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 11th
Classes from 09:00 – 17:00Introduction to the courseKey concepts related to spatial dataR’s spatial ecosystemReading data from spatial file formatsUnderstanding R’s spatial classesCreating static and interactive maps:Customizing mapsMaking facet maps

Tuesday 12th
Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting

Wednesday 13th
Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting

Thursday 14th
Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting

Jakub Nowosad
Works at: Adam Mickiewicz University
Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create, collaborate, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book.
ResearchGate
GoogleScholar
ORCID
LinkedIn
GitHub

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

ONLINE COURSE - Bayesian GLM's For Ecologists (BGFE01) This course will be delivered live
2022-06-20 - 2022-06-22 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, June 20th 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
This short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, attendees will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:

Recognise the distinction between frequentist and Bayesian approaches to model fitting
Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
Apply a 9-step protocol to fitting Bayesian GLMs
Understand and apply alternative approaches to model selection
Apply statistical modelling methods to ecological data using Bayesian GLMs

Intended Audiences
Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R
Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data

Venue
Delivered remotely

Course Information
Availability – 30 places
Duration – 3 days
Contact hours – Approx. 21 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously.

Assumed quantitative knowledge
It will be assumed that participants will be familiar with general statistical concepts and fitting GLMs to ecological data. Participants will need experience of performing statistical analysis using R.

Assumed computer background
Experience with performing statistical analyses using R and R Studio will be assumed.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
info@clovertraining.co.uk

COURSE PROGRAMME
Monday 09:00 – 16:00
Introduction to Bayesian inference

Difference between Bayesian and frequentist approaches
Bayes’ theorem
A frequentist or Bayesian framework: Which is better?
Fitting Bayesian GLMs
Steps in fitting a Bayesian GLM
Priors
Non-informative priors
Weakly informative priors
Informative priors
The posterior distribution
Bayesian computational methods
The advantages of Bayesian inference
Criticism of Bayesian inference

Data exploration

Six-step data exploration protocol
Outliers
Normality and homogeneity of the dependent variable
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Results of data exploration

Gaussian GLM with INLA

European bitterling territoriality
State the question
Selection of a statistical model
Specification of priors
Model fitting
Obtain the posterior distribution
Conduct model checks
Interpret and present model output
Visualise the results
Presenting results
Conclusions

Tuesday 09:00 – 16:00
Poisson GLM with INLA

Stickleback lateral plate number
State the question
Selection of a statistical model
Specification of priors
Model fitting
Obtain the posterior distribution
Conduct model checks
Interpret and present model output
Visualise the results
Presenting results
Conclusions

Negative binomial GLM with INLA

Coral abundance
State the question
Selection of a statistical model
Specification of priors
Model fitting
Obtain the posterior distribution
Conduct model checks
Interpret and present model output
Visualise the results
Presenting results
Conclusions

Bernoulli GLM with INLA

Cuckoo parasitism of reed warbler nests
State the question
Selection of a statistical model
Specification of priors
Model fitting
Obtain the posterior distribution
Conduct model checks
Interpret and present model output
Visualise the results
Presenting results
Conclusions

Wednesday 09:00 – 16:00
Gamma GLM with INLA

Stickleback lateral plate number
State the question
Selection of a statistical model
Specification of priors
Model fitting
Obtain the posterior distribution
Conduct model checks
Interpret and present model output
Visualise the results
Presenting results
Conclusions

Implementing and assessing Bayesian GLMs

Prior information
Presenting results of Bayesian GLMs
Reviewing Bayesian GLMs
Misuse of Bayesian GLMs
Conclusions

Discussion & questions

Course Instructor

Dr. Carl Smith

Teaches:

Bayesian GLMs for Ecologists (BGFE01)

ONLINE COURSE - Statistics For Biodiversity And Conservation (SFBC01) This course will be delivered live
2022-05-30 - 2022-06-03 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, May 30th 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
The way statistics are used in biology, and especially ecology, is changing, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging.
An understanding of statistical modelling is no longer a luxury, and it is an expectation that postgraduates and post-doctoral researchers, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software, such as R, that permits users to wrangle, analyse and visualise their data in novel ways.
This course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, you will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:

Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
Recognise common problems associated with analysis of ecological data and how to address them
Understand and apply alternative approaches to model selection
Apply statistical modelling methods to ecological data using GLMs
Recognise the distinction between frequentist and Bayesian approaches to model fitting

Intended Audiences
Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R
Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data

Venue
Delivered remotely

Course Details
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously.

Assumed quantitative knowledge
It will be assumed that participants have a basic familiarity with general statistical concepts, linear models, and statistical inference. Participants may have limited experience of performing statistical analysis using R.

Assumed computer background
Some experience with R and R Studio will be needed to run R script and install R packages, though guidance will be provided on basic concepts.

Equipment and software requirements
A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.
A full list of required packages will be made available to participants prior to the course.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously

Tickets

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 30th
Classes from 09:00 to 17:00
Introduction to R and RStudio

Getting started with R and RStudio
Basic points
Navigating RStudio
Basic settings in RStudio
Basic principles in R
Setting the working directory
Importing data
Functions and packages in R

Data exploration

Six-step data exploration protocol
Outliers
Normality and homogeneity of the dependent variable
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Results of data exploration

Testing differences between two groups

European hedgehogs
Outliers
Normality and homogeneity of the dependent variable
Zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Results of data exploration
Comparing two groups of normal unpaired data: unpaired t-test
Comparing two groups of normal paired data: the paired t-test
Comparing two groups of non-normal unpaired data: the Mann-Whitney test
Comparing two groups of non-normal paired data: the Wilcoxon test
Presenting results

Testing association between two continuous variables: correlation

Barn owls
Outliers
Normality of the variables
An excess of zeros
Multicollinearity among covariates
Relationships between variables
Independence of variables
Results of data exploration
Testing association between two continuous normal variables: Pearson’s correlation
Testing association between two continuous non-normal variables: Spearmann’s rank correlation
Testing association between two continuous non-normal variables with small sample size and ties: Kendall’s Tau correlation
Presenting the results

Tuesday 31st
Classes from 09:00 to 17:00
Modelling two continuous variables with linear regression

Northern pike length-fecundity relationship
Outliers
Normality and homogeneity of the variables
An excess of zeros
Multicollinearity among covariates
Relationship between variables
Independence of variables
Results of data exploration
Bivariate linear regression
Model validation
Homogeneity of variance of the residuals
Normality of residuals
Plot of the linear regression model
Absence of influential observations
Conclusions from model validation
Data transformation
Refit linear regression with transformed data
Model re-validation
Homogeneity of variance of the residuals
Normality of residuals
Plot of the linear regression model
Absence of influential observations
Model presentation and interpretation

Gaussian General Linear Model (GLM)

Diet of weatherfish in different seasons
Data exploration
Outliers
Normality and homogeneity of the variables
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Model fitting
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Absence of influential observations
Model presentation

Wednesday 1st
Classes from 09:00 to 17:00
Modelling two continuous variables with linear regression

Northern pike length-fecundity relationship
Outliers
Normality and homogeneity of the variables
An excess of zeros
Multicollinearity among covariates
Relationship between variables
Independence of variables
Results of data exploration
Bivariate linear regression
Model validation
Homogeneity of variance of the residuals
Normality of residuals
Plot of the linear regression model
Absence of influential observations
Conclusions from model validation
Data transformation
Refit linear regression with transformed data
Model re-validation
Homogeneity of variance of the residuals
Normality of residuals
Plot of the linear regression model
Absence of influential observations
Model presentation and interpretation

Gaussian General Linear Model (GLM)

Diet of weatherfish in different seasons
Data exploration
Outliers
Normality and homogeneity of the variables
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Model fitting
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Absence of influential observations
Model presentation

Thursday 2nd
Classes from 09:00 to 17:00
Poisson Generalised Linear Model (GLM)

Abundance of freshwater mussels
Data exploration
Outliers
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Model fitting
Model validation
Overdispersion
Model misfit
Simulating from the model
Model presentation

Negative binomial Generalised Linear Model (GLM)

Species diversity of chironomids
Data exploration
Outliers
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Model fitting
Model validation
Overdispersion
Model presentation

Friday 3rd
Classes from 09:00 to 17:00
Gaussian Generalised Linear Mixed Model (GLMM)

Body condition of European tree frogs
Data exploration
Outliers
Normality and homogeneity of the dependent variable
Lots of zeros in the response variable
Multicollinearity among covariates
Relationships among dependent and independent variables
Independence of response variable
Results of data exploration
Model fitting
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Absence of influential observations
Refit model
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Absence of influential observations
Refit model with random term
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Model presentation

Bayesian inference

Introduction to Bayesian inference
European bitterling territoriality
Data exploration
Outliers
Normality and homogeneity of the dependent variable
Lots of zeros in the response variable
Independence of response variable
Model fitting
INLA
Posterior (marginal) distributions
Comparison with frequentist Gaussian GLM
Model validation
Homogeneity of residual variance
Model misfit
Normality of residuals
Model presentation

Course Instructor

Dr. Carl Smith
Senior Lecturer, Psychology Department, Nottingham Trent University
Teaches:

Statistics for biodiversity and conservation (SFBC01)
Bayesian GLMs for Ecologists (BGFE01)

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

ONLINE COURSE – Statistical Radiocarbon Dating And Age Depth Modelling (RDAD01) This course will be delivered live
2022-05-18 - 2022-05-20 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, May 18th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
This course will provide attendees with the basics to understand and implement age-depth models for partially dated stratigraphic data. The focus will be on radiocarbon dating but the approach extends to many other forms of dated information, and will be relevant to those who have a wide variety of palaeo-environmental reconstruction problems. As is common in age-depth modelling, the Bayesian paradigm will be used to create the age-depth models, though no prior experience with Bayesian software or methods is required. The course will cover the use of multiple different R packages though the focus will be on the author’s own Bchron software. Attendees are encouraged to bring their own data sets and explore them using the tools covered during the course.

Intended Audiences
Research postgraduates, practicing academics, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data.

Venue
Delivered remotely

Course Details
Time zone – Western European Time
Availability – 30 places
Duration – 3 days
Contact hours – Approx. 21 hours
ECT’s – Equal to 2 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

Teaching Format
A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
Basic knowledge of statistics and statistical models (e.g. generalised linear modelling) required. Basic experience of exploring data sets, and fitting regression and generalised linear models with R required.

Assumed computer background
Some familiarity with R including the ability to import/export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Wednesday 18th – Classes from 09:30 – 17:30Day 1: Introduction to Radiocarbon dating; introduction to Bayesian statistics; basics of radiocarbon calibration
Thursday 19th – Classes from 09:30 – 17:30Day 2: Methods for calibrating radiocarbon dating; introducing prior information into radiocarbon date; basics of age-depth modelling
Friday 20th – Classes from 09:30 – 17:30Day 3: Age-depth modelling approaches (Bacon, Bchron, Clam, Oxcal); extending and using age-depth models in palaeo-environmental reconstruction

Course Instructor

Prof. Andrew Parnell
Works at – Teaches –

ONLINE COURSE - Machine Learning and Deep Learning Using Python (PYML03) This course will be delivered live
2022-05-18 - 2022-05-19 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, May 18th, 2022

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in machine learning and for deep learning. In this two day course, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice, and for this, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems, then look at decision trees and random forests, then look at unsupervised learning methods, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this, we will using the PyTorch deep learning toolbox. Here, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks.

Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Details
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts.The course will take place online using Zoom. On each day, the live video broadcasts will occur between (UK local time) at:• 10am-12pm• 1pm-3pm• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts, even if all of them are not convenient times. By joining any live sessions that are possible, this will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions. Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.

Assumed quantative knowledge
We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra, calculus,probability distributions. However, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics, or even advanced high school level, can be assumed to havesufficient familiarity with these concepts.

Assumed computer background
We assume familiarity with using Python, general purpose programming in Python, and numerical programming in Python. Note that both of these topics covered comprehensively in two preceding two-day courses, which together will provide all the computing prerequisites for this course.

Equipment and software requirements
Attendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach.
If you prefer to install and use Python on your machine, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properly.

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Wednesday 18th
Classes from 10:00 to 18:00
• Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning, deep learning, big data, data science have become widely used and celebrated in the last 10 years. However, their definitions are relatively nebulous, and how they related to one another and to major fields like artificial intelligence and general statistics are not simple matters. In this introduction, we briefly overview the field of machine learning and deep learning, discussing its major characteristics and sub-topics, and also discuss the prominence of Python in the area.
• Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine learning, and is usually the first topic covered in introductions to machine learning. Here, we will cover image classification (itself a “hello world” type problem in machine learning), and particularly focus on logistic regression and support vector machines (SVMs).
• Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated, which often leads to much improved performance.

Thursday 19th
Classes from 10:00 to 18:00
• Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in unclassified data. Here, we will look at two of the most widely used unsupervised techniques: k-means clustering and probabilistic mixture models.
• Topic 5: Introducing artificial neural networks and deep learning with PyTorch. Python provides many popular libraries for artificial neural networks and deep learning. These include Keras and TensorFlow. Here, we will use PyTorch, which is a relatively new but increasingly high-level neural network model building and training language. These models often use graphical processing units (GPUs) for computing. Given that most personal computers don’t have adequate GPUs, we will use Google’s Colaboratory https://colab.research.google.com/,which is a free Python Jupyter notebook service from Google.
• Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful, yet relatively easy to understand, artificial neural networks. They are also the simplest type of deep learning model. Here, we will build and train a multilayer perceptron for a classification problem.
• Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at image classification, primarily due to their translation invariance, which is highly suitable for computational vision. Here, we use PyTorch to build and train a CNN for image classification.

Course Instructor

Dr. Mark Andrews
ResearchGate
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

FREE SEMINAR - Bayesian GLM's For Ecologists (BGFE01S)
2022-05-17 15:00 - 15:30 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Registration is now closed, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.
Event Date
​

Free seminar

This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Bayesian GLM’s for Ecologists”.

Time

15:00-15:30 GMT+1

Speaker

Course Instructor Dr. Carl Smith

About this course
This short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, attendees will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:

Recognise the distinction between frequentist and Bayesian approaches to model fitting
Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
Apply a 9-step protocol to fitting Bayesian GLMs
Understand and apply alternative approaches to model selection
Apply statistical modelling methods to ecological data using Bayesian GLMs

ONLINE COURSE – Bayesian GLM’s For Ecologists (BGFE01) This course will be delivered live

Tickets

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Tickets are no longer available

Dr. Carl Smith
Senior Lecturer, Psychology Department, Nottingham Trent University
Teaches:

Statistics for biodiversity and conservation (SFBC01)
Bayesian GLMs for Ecologists (BGFE01)

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

ONLINE COURSE – Introduction To Scientific, Numerical, And Data Analysis Programming In Python (PYSC03) This course will be delivered live
2022-05-04 - 2022-05-05 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, May 4th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of scientific, numerical, and data analysis programming Python. This two day course provides a general introduction to numerical programming in Python, particularly using numpy, data processing in Python using Pandas, data analysis in Python using statsmodels and rpy2. We will also cover the major data visualization and graphics tools in Python, particularly matplotlib, seaborn, and ggplot. Finally, we will cover some other major scientific Python tools, such as for symbolic mathematics and parallel programming and code acceleration. Note that in this course, we will not be teaching Python fundamentals and general purpose programming, but this knowledge will be assumed, and is also provided in a preceding two-day course.

Intended Audiences
This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here, but are covered in a subsequent course, the fundamentals taught here are vital for master data science and scientific applications of Python.

Venue
Delivered remotely

Course Details
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts.
The course will take place online using Zoom. On each day, the live video broadcasts will occur between (UK local time) at:• 10am-12pm• 1pm-3pm• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts, even if all of them are not convenient times. By joining any live sessions that are possible, this will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions. Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.

Assumed quantitative knowledge
We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra, calculus,probability distributions. However, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics, or even advanced high school level, can be assumed to havesufficient familiarity with these concepts.

Assumed computer background
We assume familiarity with using Python and knowledge of general purpose programming in Python. This topics are covered comprehensively in a preceding two-day course, which will provide all the prerequisites for this course.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME
Wednesday 4th – Classes from 10:00 to 18:00
Topic 1: Numerical programming with numpy. Although not part of Python’s official standard library, the numpy package is the part of the de facto standard library for any scientific and numerical programming. Here we will introduce numpy, especially numpy arrays and their built in functions (i.e. “methods”). Here, we will also consider how to speed up numpy code using the Numba just-in-time compiler.
Topic 2: Data processing with pandas. The pandas library provides means to represent and manipulate data frames. Like numpy, pandas can be see as part of the de facto standard library for data oriented uses of Python. Here, we will focus on data wrangling including selecting rows and columns by name and other criteria, applying functions to the selected data, aggregating the data. For this, we will use Pandas directly, and also helper packages like siuba.
Thursday 5th – Classes from 10:00 to 18:00
Topic 3: Data Visualization. Python provides many options for data visualization. The matplotlib library is a low level plotting library that allows for considerable control of the plot, albeit at the price of a considerable amount ofm low level code. Based on matplotlib, and providing a much higher level interface to the plot, is the seaborn library. This allows us to produce complex data visualizations with a minimal amount of code. Similar to seaborn is ggplot, which is a direct port of the widely used R based visualization library.
Topic 4: Statistical data analysis. In this section, we will describe how to perform widely used statistical analysis in Python. Here we will start with the statsmodels, which provides linear and generalized linear models as well as many other widely used statistical models. We will also cover rpy2, which is and interface from Python to R. This allows us to access all of the the power of R from within Python.
Topic 5: Symbolic mathematics. Symbolic mathematics systems, also known as computer algebra systems, allow us to algebraically manipulate and solve symbolic mathematical expression. In Python, the principal symbolic mathematics library is sympy. This allows us simplify mathematical expressions, compute derivatives, integrals, and limits, solve equations, algebraically manipulate matrices, and more.
Topic 6: Parallel processing. In this section, we will cover how to parallelize code to take advantage of multiple processors. While there are many ways to accomplish this in Python, here we will focus on the multiprocessing

Course Instructor

Dr. Mark Andrews

Works AtSenior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

ONLINE COURSE – Introduction To Python And Programming In Python (PYIN03) This course will be delivered live
2022-04-27 - 2022-04-28 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Wednesday, April 20th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course

Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment, the Python language, and general purpose programming in Python. We cover how to install and set up a Python computing environment, describing how to set virtual environments, how to use Python package installers, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python, covering all the following major topics: data types and data container types, conditionals, iterations, functional programming, object oriented programming, modules, packages, and imports. Note that in this course, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course, for which the topics covered in this course are a necessary prerequisite.

Intended Audiences

This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here, but are covered in a subsequent course, the fundamentals taught here are vital for master data science and scientific applications of Python.

Venue
Delivered remotely

Course Details
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts.
The course will take place online using Zoom. On each day, the live video broadcasts will occur between (UK local time) at:
• 10am-12pm
• 1pm-3pm
• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts, even if all of them are not convenient times. By joining any live sessions that are possible, this will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions. Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.

Assumed quantitative knowledge
No particular knowledge of mathematics or statistics is required.

Assumed computer background

No prior experience with Python or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.

Equipment and software requirements

Attendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach.
If you prefer to install and use Python on your machine, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properly.

Tickets

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME
Wednesday 27th
Classes from 10:00 to 18:00
Topic 1: Installing and setting up Python. There are many ways to write and execute code in Python. Which to use depends on personal preference and the type of programming that is being done. Here, we will explore some of the commonly used Integrated Development Environments (IDE) for Python, which include Spyder and PyCharm. Here, we will also introduce Jupyter notebooks, which are widely used for scientific applications of Python, and are an excellent tool for doing reproducible interactive work. Also as part of this topic, we will describe how to use virtual environments and package installers such as pip and conda.
Topic 2: Data Structures. We will begin our coverage of programming with Python by introducing its different data structures.and operations on data structures This will begin with the elementary data types such as integers, floats, Booleans, and strings, and the common operations that can be applied to these data types. We will then proceed to the so-called collection data structures, which primarily include lists, dictionaries, tuples, and sets.
Topic 3: Programming I. Having introduced Python’s data types, we will now turn to how to program in Python. We will begin with iteration, such as the for and while Here, we also cover some of Python’s functional programming features, specifically list, dictionary, and set comprehensions.
Thursday 28th
Classes from 10:00 to 18:00
Topic 4: Programming II. Having covered iterations, we now turn to other major programming features in Python, specifically, conditionals, functions, and exceptions.
Topic 5: Object Oriented Programming. Python is an object oriented language and object oriented programming in Python is extensively used in anything beyond the very simplest types of programs. Moreover, compared to other languages, object oriented programming in Python is relatively easy to learn. Here, we provide a comprehensive introduction to object oriented programming in Python.
Topic 6: Modules, packages, and imports. Python is extended by hundreds of thousands of additional packages. Here, we will cover how to install and import these packages, but more importantly, we will show how to write our own modules and packages, which is remarkably easy in Python relative to some programming languages.

Course Instructor

Dr. Mark Andrews

Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

ONLINE COURSE – Advances In Spatial Analysis Of Multivariate Ecological Data: Theory And Practice (MVSP05) This course is pre-recorded with live help
2022-04-25 - 2022-04-28 | Delivered remotely (Canada)
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, April 25th, 2022

Course Format
This is a ‘PRE-RECORDED’ course, lectures are pre-recorded and shared via zoom. The instructors will be available for live help with practicals and to answer any questions. A good internet connection is essential.
Time Zone
TIME ZONE – Multiple timezones – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity, which is the spatial variation of communities. Researchers in spatial ecology, population genetics and landscape genetics will find these methods useful as they are applicable to all types of communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The new methods, collectively referred to as spatial eigen-function analysis, are grounded into techniques commonly used by community ecologists, which will be described first: simple ordination (PCA, CA, PCoA), multivariate regression and canonical analysis, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data.

Intended Audiences
Research postgraduates, practicing academics and primary investigators in spatial ecology particularly communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The skills learnt can also be applied by management and environmental professionals in government and industry.

Venue
Delivered remotely

Course Details
Time Zone –Multiple timezones
Availability – TBC
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course will be a combination of pre-recorded lectures delivered by Prof. Pierre Legendre, Practical sessions with live support via email or video link and final live summary with Q and A at the end of each day Prof Pierre Legendre.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
The recordings will be aired via zoom. Recordings last for approx. 4 hours. Followed by 3-hour practical. And then an approx. 1 hour (we have more time if needed) Q and A session with Pierre Legendre.
Recordings will be aired to accommodate different time zones listed below in GMT.
Group 1Recordings 08:00 – 12:00Practical 12:30 – 15:30
Group 2Recordings 12:00 – 16:00Practical 16:30 – 19:30
Group 1 and 2Live Q and A – 20:00 – 21:00 (with Prof. Pierre Legendre)
You can use this link to find a time zone which suites you best.
https://www.timeanddate.com/worldclock/?query=gmt
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.

Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 25th
Lesson 1: Ordination in reduced spaceSection 1.0. Ordination in reduced space: An introductionSection 1.1. Principal component analysis (PCA)Section 1.2. Correspondence analysis (CA)Section 1.3. Principal coordinate analysis (PCoA)Section 1.4. Metric ordination methods in ecology (included with 1.3)
Practical 1

Tuesday 26th
Lesson 2: Dissimilarities and transformations
Lesson 3: Tests of statistical significance
Lesson 4: Linear regressionSection 4.1 Multiple linear regressionSection 4.2 Partial regression and variation partitioning
Practical 2

Wednesday 27th
Lesson 5: Canonical analysis
Lesson 6: Beta diversitySection 6.1. Partitioning beta diversitySection 6.2. Replacement and richness differenceSection 6.3. Temporal beta diversity
Practical 3

Thursday 28th
Lesson 7: Spatial modellingSection 7.1. Origin of spatial structures in ecologySection 7.2. Spatial eigenfunction modellingSection 7.3. Space-time interaction
Lesson 8: Mantel test in spatial analysis
Practical 4

Course Instructor

Prof. Pierre Legendre
Coming Soon…

FREE SEMINAR - Introduction To Multivariate Analysis In Ecology And Evolutionary Biology using R (IMAE01S) This course will be delivered live
2022-04-06 13:30 - 14:30 | Delivered remotely (Finland), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Online registration has now closed, please email oliverhooker@prstatistics.com to be added to the seminar or to receive a link to the recording

Free seminar

This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Introduction to Multivariate Analysis in Ecology and Evolutionary Biology using R”

Time

13:30-14:00 Eastern European Standard Time

Speaker

Course Instructor Dr. Antoine Becker-Scarpitta

About this course
This community analytics course is designed for students who have recently started their projects or researchers who are starting using the R ecosystem. During this three-day course, we will cover the basic concepts of multivariate analysis and their implementation in R. This course is a complement to the PR Statistic offering allowing also beginners and non-programmers to discover the statistical tools needed to analyze an ecological dataset in research, natural resource management or conservation context. This course is not geared toward any particular taxonomic group or ecological system.
We will cover diversity indices, distance measures and multivariate distance-based methods, clustering, classification, and ordination techniques. We will focus on the concept of the methods and their implementation on R using different R packages. We will use real-world examples to implement analyses, such as describing patterns along gradients of environmental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors, data mining. The course will consist of lectures, work on R code scripts, and exercises for participants.

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
info@clovertraining.co.uk

FREE SEMINAR – Advances in Spatial Analysis of Multivariate Ecological Data: Theory and Practice (MVSP05S)
2022-03-23 18:00 - 18:30 | Delivered remotely (Canada)
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Registration is now closed, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.

Free seminar
This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Advances In Spatial Analysis Of Multivariate Ecological Data: Theory And Practice”.
Time
18:00 GMT
Speaker
Course Instructor Prof. Pierre Legendre
About this course
The course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity, which is the spatial variation of communities. These methods are applicable to all types of communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The new methods, collectively referred to as spatial eigen-function analysis, are grounded into techniques commonly used by community ecologists, which will be described first: simple ordination (PCA, CA, PCoA), multivariate regression and canonical analysis, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data.

Tickets

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Tickets are no longer available

Please email any course enquiries to oliverhooker@prstatistics.com

FREE SEMINAR - Statistics For Biodiversity And Conservation (SFBC01S)
2022-03-23 17:00 - 17:30 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Registration is now closed, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.

Free seminar

This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Statistics for Biodiversity and Conservation”.

Time

TBC

Speaker

Course Instructor Dr. Carl Smith and Dr. Mark Warren

Course description
The way statistics are used in biology, and especially ecology, is changing, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging.
An understanding of statistical modelling is no longer a luxury, and it is an expectation that postgraduates and post-doctoral researchers, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software, such as R, that permits users to wrangle, analyse and visualise their data in novel ways.
This course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, you will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:

Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
Recognise common problems associated with analysis of ecological data and how to address them
Understand and apply alternative approaches to model selection
Apply statistical modelling methods to ecological data using GLMs
Recognise the distinction between frequentist and Bayesian approaches to model fitting

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Dr. Carl Smith
Senior Lecturer, Psychology Department, Nottingham Trent University
Teaches:

Statistics for biodiversity and conservation (SFBC01)
Bayesian GLMs for Ecologists (BGFE01)

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

ONLINE COURSE - GIS And Remote Sensing Analyses With R (GARM01) This course will be delivered live
2022-02-14 - 2022-02-18 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live

Event Date
Monday, February 14th, 2022

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – Western European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
The course will cover the basics to perform spatial analyses using R as a Geographical Information System (GIS) platform and Remote Sensing as main data source. The course will provide a brief theoretical background of GIS tools and Remote Sensing data and techniques. By the end of this 4-day practical course, attendees will have the capacity to search satellite imagery, to manipulate Remote Sensing data, to create new variables, as well as to choose the best spatial tools and techniques to perform spatial analyses and interpret their results.
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use the Rpackage RSToolbox for Remote Sensing image processing and analysis such as calculating spectral indices, principal component transformation, or unsupervised and supervised classification.

Intended Audiences
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines.

Venue
Delivered remotely

Course Details
Availability – 25 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and applications of GIS and Remote Sensing.Practical lectures on most used spatial tools. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practical modules will be provided by the instructor, but participants are welcome to bring their own data.

Assumed quantitative knowledge
Basic knowledge in Geographical Information Systems, Remote Sensing, and spatial analyses.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

Course Programme

Monday 21st
Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files.

Tuesday 22nd
Classes from 09:30 – 17:00Theory – Vector database operations.Practical – Attribute and spatial queries: join/merge, filter/subset, select by attribute, select bylocation, summarize, add/calculate new attributes (columns), plot attributes.Theory – Vector analyses.P: Vector analyses – buffer, merge, dissolve, intersect, union, select, calculate areas.

Wednesday 23rd
Classes from 09:30 – 17:00Theory – Raster GIS.Practical – Raster analyses: rasterize, crop, mask, merge, distance surface, zonal statistics.Theory – Introduction to Remote Sensing. RS as main data source: RS sensors & variables.RS software.Practical – Getting and plotting RS data. Downloading, reading, and plotting RS data in R.Manipulating satellite data.

Thursday 24th
Classes from 09:30 – 17:00Theory – Working with RS variables. Image classification, Vegetation indexes, data fusion.Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classificationmethods.Theory: Remote Sensing applications to biologyPractical: Statistical analyses with RS data.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal websiteWork WebpageResearchGateGoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)
