PR Statistics

FREE SEMINAR - Bayesian GLM's For Ecologists (BGFE01S)
2022-05-17 15:00 - 15:30 (Europe/London) | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) 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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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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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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
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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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 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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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 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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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

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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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 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 – Trait based ecology Using R: Theory and Practice (TBER01) 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 – Trait based ecology Using R: Theory and Practice (TBER01) 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

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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 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)

ONLINE COURSE - Introduction To Stan For Bayesian Data Analysis (ISBD01) This course will be delivered live
2022-01-18 - 2022-01-20 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Tuesday, January 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 (note the later times to accommodate attendees from the Americas) – 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
Stan (https://mc-stan.org) is “a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.” Stan is a powerful programming language for developing and fitting custom Bayesian statistical models. In this course, we provide a general introduction to the Stan language, and describe how to use it to develop and run Bayesian models. We begin by first covering the theory behind Stan, which covers Bayesian inference, Markov Chain Monte Carlo (MCMC) for sampling from probability distributions, and the efficient Hamiltonian Monte Carlo (HMC) method that Stan implements. Next, we learn how to write Stan models by creating simple Bayesian such as binomial models and models using normal distributions. In so doing, the basics of the Stan language will be apparent. Although Stan can be used with multiple different type of statistical programs (Python, Julia, Matlab, Stata), we will use Stan with R exclusively, specifically using the rstan or cmdstanr packages. Using thesepackages, we will can compile and sample from a HMC sampler for the Bayesian models we defined, plot and summarize the results, evaluate the models, etc. We then cover some widely used and practically useful models including linear regression, logistic regression, multilevel and mixed effects models. We will end by covering some more complex models, including probabilistic mixture models.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES

Intended Audiences
This course is aimed at anyone who is in interested in doing advanced Bayesian data analysis using Stan. Stan is a state of the art tool for advanced analysis across all academic scientific disciplines, engineering, and business, and other sectors.

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 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 (Eastern Standard Time) between 12:00-17:00.
All sessions will be video recorded and made available to all attendees as soon as possible.
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.
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 from 5pm-9pm. 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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and practical experience with linear regression, logistic regression, mixed effects models using R.

Assumed computer background
Some experience and familiarity with R is required. No prior experience with Stan itself is required.

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 17:00 to 21:00
Topic 1: Hamiltonian Monte Carlo for Bayesian inference. We begin by describing Bayesian inference, whose objective is the calculation of a probability distribution over a high dimensional space, namely the posterior distribution. In general, this posterior distribution can not be described analytically, and so to summarize or make predictions from the posterior distribution, we must draw samples from it. For this, we can use Markov Chain Monte Carlo (MCMC) methods including the Metropolis sampler, sometimes known as random-walk Metropolis. Hamiltonian Monte Carlo (HMC), which Stan implements, is ultimately an efficient version of the Metropolis sampler that does not involve random walk behaviour. In this introductory section of the course, we will go through these major theoretical topics in sufficient detail to be able to understand how Stan works.
Topic 2: Univariate models. To learn the Stan language and how to use it to develop Bayesian models, we will start with simple models. In particular, we will look at binomial models and models involving univariate normal distributions. The models will allow us to explore many of the major features of the Stan language, including how to specify priors, in conceptually easy examples. Here, we will also learn how to use rstan and cmdstanr to compile the HMC sampler from the defined Stan model, and draw samples from it.
Wednesday 19th
Classes from 17:00 to 21:00
Topic 2: Univariate models continued
Topic 3: Regression models. Having learned the basics of Stan using simple models, we now turn to more practically useful examples including linear regression, general linear models with categorical predictor variables, logistic regression, Poisson regression, etc. All of these examples involve the use of similar programming features and specifications, and so they are easily extensible to other regression models.
Thursday 20th
Classes from 17:00 to 21:00
Topic 4: Multilevel and mixed effects models. As an extension of the regression models that we consider in the previous topic, here we consider multilevel and mixed effects models. We primarily concentrate on linear mixed effects models, and consider the different ways to specify these models in Stan.
Topic 5: Because Stan is a programming language, it essentially gives us the means to create any bespoke or custom statistical model, and not just those that are widely used. In this final topic, we will cover some more complex cases to illustrate it power. In particular, we will cover probabilistic mixture models, which are a type of latent variable model.

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 - Remote Sensing With Aircraft And Drone LiDAR Sensors (RSLD01) This course will be delivered live
2021-12-13 - 2021-12-17 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, December 12th, 2021

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

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.

Intended Audiences

Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills.

Venue
Delivered remotely

Course Details
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 24 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format

The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with computing exercises on the processing of LiDAR data and point clouds. Afternoon practicals will be based on the topics covered in the morning lectures.

Assumed quantitative knowledge

Familiarity with Geographic Information Systems and geospatial data (i.e., raster and vector data) could be useful, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful, but not required.

Assumed computer background

No prior experience with LiDAR processing software, point cloud data or any programming language is required.

Equipment and software requirements

Attendees of the course must use a computer with any Operating System installed (GNU/Linux, MS Windows or MacOS). The course will use Open-Source software (FOSS) and some proprietary software which will be downloaded, installed and configured during the lectures.

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 6th February – Classes from 10:00 to 17:00

Module 1: Fundamentals of Light Detection and Ranging (LiDAR) technique. Theoretical principles of a LiDAR systems. Electronic and sensor components. Main differences between spatial, aerial and terrestrial platforms. The physics of laser signals: Introduction to discrete and full-waveform LiDAR and signal return analysis. Resolutions and precisions achieved. Advantages and disadvantages of the technique. Practice: Introduction to LiDAR data, platforms and services. Overview of the available processing software and programming languages/libraries.

Tuesday 7th February – Classes from 10:00 to 17:00

Module 2: Interpretation of LiDAR data. Introduction to metrics/products such as Digital Elevation Models, Digital Terrain Models and Canopy Height Models. Tree delineation approaches and algorithms (ex. Watershed Algorithm). Discrete versus full-waveform LiDAR data. Echo Decomposition for peak point extraction. Voxelisation of full-waveform LiDAR data. Introduction to binary files: Discrete and full-waveform LiDAR LAS files formats. Practice: Tridimensional point cloud processing and analysis. Filtering, measuring and classification of LiDAR point clouds.

Monday 13th February – Classes from 10:00 to 17:00

Module 3: Managing and exploring a LAS dataset. Visualization advanced techniques, metadata analysis and content reports, LiDAR points classification into ground points and non-ground points, buildings and high vegetation classification. Coordinate Reference System transforms. LIDAR points triangulation into a TIN in order to create a Digital Elevation Model. Elevation contours extraction from a LiDAR point cloud and boundary polygon extraction. RGB colour sampled from an orthomosaic.

Tuesday 14th February – Classes from 10:00 to 17:00

Module 4: Different applications for LiDAR data: biodiversity monitoring, forest health monitoring, urban planning, wood trade, archaeology and heritage monitoring and automated driving. Other types of LiDAR systems: Space-based liDAR for measuring ice sheet mass balance, cloud and aerosol heights. Bathymetric LiDAR for the study of underwater depth of ocean floors. Practice: Post-processing of LiDAR products, Digital Terrain Model and elevation profile analysis. Measurements of distances, areas and volumes. Integration with external geospatial data in a Geographic Information System (GIS).

Course Instructor

Dr. Nelson Pires

– Works at: University of Porto, Portugal

– Delivers:

Remote Sensing with satellite multi-spectral sensors (RSMS01)

Remote Sensing with drone RGB and Near Infrared cameras (RSWD01)

Remote Sensing with aircraft and drone LiDAR sensors (RSLD)

Nelson holds a degree in Physics and Surveying Engineering, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics.

ORCID: https://orcid.org/0000-0002-6629-8060

FREE RECORDED 1 DAY INTRO TO R AND R STUDIO (FIRR01)
2021-10-20 - 2021-10-22 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Wednesday, April 13th, 2022

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 Python And Programming In Python”.

Time
17:00 GMT

Speaker

Course Instructor Dr. Mark Andrews
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.

About This Course
In this free one day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc.
IF YOU ARE IN A POSITION TO CONTRIBUTE TOWARDS YOUR PLACE IT WOULD BE APPRECIATED SO WE CAN USE ANY REVENUE TO FUND OTHER FREE COURSES – THERE IS THE OPTION TO PURCHASE A TICKET FOR £20.00
WE ALSO RECOMMEND ORGANISING A SAMALL GROUP IF YOU HAVE COLLEAGUES WHO ALSO WANT TO ATTEND, THIS WAY WE CAN MAXIMISE HOW MANY PEOPEL CAN ACCESS THE COURSE
THIS IS A FREE INTRODUCTORY COURSE – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE PAID COURSES IN THIS SERIES

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.

Course Details
Venue – Delivered remotely
Time zone – NA
Availability – NA
Duration – 1 days
Contact hours – Approx. 6 hours
ECT’s – NA
Language – English

Teaching Format
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
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.

Assumed computer background
No prior experience with R 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 will need to use a computer on which RStudio can be installed. This includes Mac, Windows, and Linux, but not tablets or other mobile devices. Instructions on how to install and configure all the required software, which is all free and open source, will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.

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 Programme

Topic 1
The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular.

Topic 2
Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively.

Topic 3
First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier.

Mark Andrews

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 - Remote Sensing With Satellite Multi-Spectral Sensors (RSMS01) This course will be delivered live
2021-09-20 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, September 20th, 2021

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

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.

Intended Audiences

Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills.

Venue
Delivered remotely

Course Information
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 24 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format

The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with computing exercises on the processing of Earth Observation data. Afternoon practicals will be based on the topics covered in the morning lectures.

Assumed quantitative knowledge

Familiarity with Geographic Information Systems and geospatial data (i.e., raster and vector data) could be useful, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful, but not required.

Assumed computer background

No prior experience with Remote Sensing software and data or any programming language is required. Familiarity with any digital image processing technique will be helpful, but is not required.

Equipment and software requirements

Attendees of the course must use a computer with any Operating System installed (GNU/Linux, MS Windows or MacOS). The course will use only Open-Source software (FOSS) which will be downloaded, installed and configured during the lectures.

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 26th September – Classes from 10:00 to 17:00

Module 1: Fundamentals of Remote Sensing. Concepts of satellite orbits, spacial resolutions, temporal resolutions, spectral and radiometric resolutions. Different types of sensors and processing levels of Earth Observation satellites. The physics of atmosphere and spectral signatures. Conceptual understanding of Remote Sensing, where the participants will be able to identify its advantages and disadvantages. Introduction to data platforms, software tools, web portals, and environmental monitoring applications. Practice: Introduction to Remote Sensing software.

Tuesday 27th September – Classes from 10:00 to 17:00

Module 2: Earth Observation Programmes. The National Aeronautics and Space Administration (NASA) LANDSAT Program and the European Space Agency (ESA) Copernicus/SENTINEL Program. History and Objectives. Satellite missions chronology. Different spatial, temporal, spectral and radiometric resolutions. LANDSAT Multispectral Scanner (MSS) and SENTINEL-2 Multispectral Instrument (MSI) sensor designs. Uses of Earth Observation satellite imagery for natural resources management, climate change, environmental disasters and ecology. Practice: Introduction to satellite image processing.

Thursday 29th September – Classes from 10:00 to 17:00

Module 3: Remote Sensing for Vegetation Monitoring and Agricultural Applications. Satellite observations to assess a wide variety of geophysical and biophysical parameters, including precipitation, temperature, evapotranspiration, soil moisture, and vegetation health. Band combination e index classification for vegetation monitoring. Remote Sensing data for agriculture monitoring, specifically drought and crop monitoring. Practice: Supervised and unsupervised classification methods.

Friday 30th September – Classes from 10:00 to 17:00

Module 4: Satellite Applications for Biodiversity Conservation. Specific applications and hands-on demonstrations of how to use Remote Sensing data to derive conservation policies and management decisions. Remote Sensing for Conservation and Biodiversity: Animal Movement, Dynamic Habitat Index for Biodiversity, Vegetation Carbon Stock Corridors and techniques for Land Change Detection. Land Management and Ecosystem Based Tools: Coral Reef Watch and MODIS NDVI Anomalies and Time Series. Practice: Image fusion and Pansharpening techniques.

Course Instructor

Dr. Nelson Pires

– Works at: University of Porto, Portugal

– Delivers:

Remote Sensing with satellite multi-spectral sensors (RSMS01)

Remote Sensing with drone RGB and Near Infrared cameras (RSWD01)

Remote Sensing with aircraft and drone LiDAR sensors (RSLD)

Nelson holds a degree in Physics and Surveying Engineering, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics.

ORCID: https://orcid.org/0000-0002-6629-8060

ONLINE COURSE - Missing Data Analytics (MDAR01)
2021-09-08 - 2021-09-10 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
September, January 8th, 2021

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.

Course Details
This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. 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 mice package.
The course is structured over 3 days and includes classes on:

An introduction to missing data analysis terminology, missing completely at random, missing at random, not missing at random
A revision of likelihood and regression approaches
The Fully Conditional Specification (FCS) approach
An introduction to the mice package
The use of Bayesian and likelihood-based methods in missing data analysis
Bayesian missing data analysis using JAGS
More advanced missing data analysis including non-ignorable and not missing at random methods

Intended Audiences
Research postgraduates, practicing academics, or other professionals from any field who would like to learn about missing data analysis and how it can help them produce better quality information from their data.

Venue
Delivered remotely

Course Information
Venue – Delivered remotely
Time zone – Western European Time
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

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
A basic understanding of regression methods and generalised linear models.

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
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

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

Wednesday 8th
Classes from 09:30 to 17:30
How to run a missing data analysis in mice
Introduction to Bayesian analysis and missing data
The use of Bayesian and likelihood-based methods in missing data analysis
The fully conditional specification approach to missing data analysis

Thursday 9th
Classes from 09:30 to 17:30Including missing data in JAGS and StanAn introduction to the mice packageBayesian software tools JAGS/Stan for missing data analysis

Friday 10th
Classes from 09:30 to 17:30Advanced missing data analysis methodsMore advanced missing data analysis including non-ignorable and not missing at random methodsMissing data analysis in machine learning

Course Instructor

Prof. Andrew Parnell
Coming Soon

ONLINE COURSE - Landscape genetic data analysis using R (LNDG04) This course will be delivered live
2021-08-09 - 2021-08-13 | Delivered remotely (USA), Eastern Daylight Time, MD United States, United States
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, 9th August, 2021

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 – Eastern Standard Time – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
The term ‘landscape genetics’ has been applied studies that integrate ecological context and intervening landscape into population genetic analyses of contemporary processes such as gene flow and migration. This course will cover the basics of both quantitative landscape ecology and population genetics, focusing on how we develop and evaluate spatial/genetic analyses using the R platform.

Intended Audiences
This course is suitable for graduate students, postdoctoral researchers, and primary investigators interested in learning how to integrate landscape ecological and population genetic tools using the R software.

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

Venue
Delivered Remotely

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 quantative 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

Monday 15th
Classes from 9:30 to 17:30
Module 1: Spatial & Ecological Data.Installation & configuring R & RStudioAcquiring spatial data, projections, and visualizationVector and raster data

Tuesday 16th
Classes from 9:30 to 17:30
Module 2: Genetic markers and basic analysesGenetic markers and samplingGenetic distance, diversity, and structureOrdination techniques based upon genetic markers

Wednesday 17th
Classes from 9:30 to 17:30
Module 3: Integrating spatial and genetic dataBarrier detection & population divisionResistance ModelingMantel and distance regressionsRemote sensing – LiDAR and Hyperspectral data

Thursday 18th
Classes from 9:30 to 17:30
Module 4: Integrating spatial and genetic dataSpatial autocorrelationNetwork ApproachesPCMN & Redundancy

Friday 19th
Classes from 9:30 to 17:30
Module 5: Adaptive Genetic VarianceOutliers & gradientsQuantitative genetics, why we should care.Chromosome walking

Course Instructor

Prof. Rodney Dyer
Coming Soon

ONLINE COURSE - Bayesian Approaches To Regression And Mixed Effects Models Using R and brms (BARM01)
2021-05-26 - 2021-05-27 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Thursday, May 26th, 2021

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
Bayesian methods are now increasingly widely used for data analysis based on linear and generalized linear models,and multilevel and mixed effects models. The aim of this course is to provide a solid introduction to Bayesian approaches to these topics using R and the brms package. Ultimately, in this course, we aim to show how Bayesian methods provide a very powerful, flexible, and extensible approach to general statistical data analysis. We begin by covering Bayesian approaches to linear regression. We will compare and contrast, in both practical and theoretical terms, the Bayesian approach and classical approach to linear regression. This will allow us to easily identify the major similarities and major differences, both in terms of concepts and practice, between the Bayesian and classical approaches. We will then proceed to Bayesian approaches to generalized linear models, including binary logistic regression, ordinal logistic regression, Poisson regression, zero-inflated models, etc. In this coverage, we will see the very wide range of models to which Bayesian methods can be easily applied. Finally, we will cover Bayesian approaches to multilevel and mixed effects models. Here again, we will see how Bayesian methods allow us to easily extend traditionally used methods like linear and generalized linear mixed effects models. We will also see how Bayesian methods allow us to control model complexity and solve algorithmic problems (e.g. model convergence problems) that can plague classical approaches to multilevel and mixed effects models. Throughout this course, we will be using, via the brms package, Markov Chain Monte Carlo (MCMC) methods. However, full technical details of MCMC will will be described here, but will be provided in subsequent Bayesian data analysis courses.

Intended Audiences
This course is aimed at anyone who is in interested in using Bayesian approaches to regression, multilevel, and mixedeffects models in any area of science, including the social sciences, life sciences, physical sciences. No prior experienceor familiarity with Bayesian statistics is required.

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 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 (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm
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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with linear regression, logistic regression, mixed effects models.

Assumed computer background
Some experience and familiarity with R is required. However, although we will be using R extensively, all the code that we use will be made available, and so attendees will usually just need to copy and paste and add minor modifications to this 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. 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 26th
Classes from 12:00 to 20:00
Topic 1: 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.
Topic 2: 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 assumehomogeneity 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 ofthe 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.
Thursday 27th
Classes from 12:00 to 20:00
Topic 3: Bayesian generalized linear models. Generalized linear models include models such as logistic regression, including multinomial and ordinal logistic regression, Poisson regression, negative binomialregression, 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 howpowerful 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 4: 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 willalso 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.

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.

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ONLINE COURSE - Introduction / Fundamentals of Bayesian Data Analysis statistics using R (FBDA01)
2021-05-19 - 2021-05-20 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Thursday, May 19th, 2021

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
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 teach 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 begin with a gentle introduction to all the fundamental principles and concepts of Bayesian methods: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayesian model selection, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses, specifically general linear models. For these analyses, we will use real world data sets, and carry out the analysis using the brms package in R, which is an excellent and powerful package for Bayesian analysis. In this coverage, we will also provide a brief introduction to Markov Chain Monte Carlo methods, although these will be described in more detail in subsequent Bayesian data analysis courses.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES

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 statisticsis required.

Venue
Delivered remotely

Course Information
Venue – Delivered remotely
Time zone – GMT+0
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 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 (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm
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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with commonly used methods like linear regression, correlation, or t-tests. Most or all of these concepts and methods are covered in a typical undergraduate statistics courses in any of the sciences and related fields.
Assumed computer background
R experience is desirable but not essential. Although we will be using R extensively, all the code that we use will be made available, and so attendees will just need to copy and paste and add minor modifications to this code. Attendees should install R and RStudio and some R packages on their own computers before the workshops, and have some minimal familiarity with the R environment.
Equipment and software requirements
A computer with a working version of R or RStudio is required. R and RStudio are both available as free and opensource software for PCs, Macs, and Linux computers. In addition to R and RStudio, some R packages, particularly brms, are required. Instructions on how to install R/RStudio and all required R packages will be provided before the course begins.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Coming soon..

Assumed computer background
Coming soon..

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

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

Wednesday 19th – Classes from 12:00 to 20:00
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 alternativeV 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 causesfrom some known effects. As such, it can be used as a means for performing statistical inference. In this sectionof the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all ofBayesian data analysis is based on an application of these methods to more complex statistical models, and sounderstanding these simple cases of the application of Bayes’ rule can help provide a foundation for the morecomplex 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.
Thursday 20th – Classes from 12:00 to 20:00
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 causesfrom some known effects. As such, it can be used as a means for performing statistical inference. In this sectionof the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all ofBayesian data analysis is based on an application of these methods to more complex statistical models, and sounderstanding these simple cases of the application of Bayes’ rule can help provide a foundation for the morecomplex 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.

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

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ONLINE COURSE - Introduction to statistics using R and Rstudio (IRRS03)
2021-03-17 - 2021-03-18 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Thursday, May 26th, 2021

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
In this two day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc. We then provide an introduction to data processing and formatting (aka, data wrangling), an introduction to data visualization, an introduction to RMarkdown, and introduce how to some of the most widely used statistical methods such as linear regression, Anovas, etc. From this course, you will gain a comprehensive introduction to R, which will serve as foundation for progressing further with R to any kind of data analysis, data science, or statistics.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES

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 Information
Time zone – GMT+0
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 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 (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm
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
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.
Assumed computer background
No prior experience with R 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 will need to use a computer on which RStudio can be installed. This includes Mac, Windows, and Linux, but not tablets or other mobile devices. Instructions on how to install and configure all the required software, which is all free and open source, will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Coming soon..

Assumed computer background
Coming soon..

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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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

Wednesday 17th – Classes from 12:00 to 20:00
Topic 1: The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular.
Topic 2: Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively.
Topic 3: First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier.
Thursday 18th – Classes from 12:00 to 20:00
Topic 4: Introducing wrangling. Data wrangling, which is the art of cleaning and restructuring data is a big topic. Here, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Here, we will primarily focus on filtering, slicing, selecting, renaming, and mutating data frames.
Topic 5: Data visualization. Data visualization is another big and important topics. Here, we just provide an introduction, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). We’ll cover scatterplots, boxplots, histograms, and their variants.
Topic 6: RMarkdown. RMarkdown is a powerful tool for creating reproducible research reports, as well as slides, scientific website, posters, etc. In an RMarkdown document, we mix R code and the narrative text of the report, and the outputs of the R code, including figures, are included in the final document.
Topic 7: Introduction to Statistics using R. There are many thousands of statistical methods built into R. Here, we will simply provide an introduction to some of the most widely used methods. In particular, we will cover linear regression, Anova, and some other simple test. The aim of this section is to get a sense of how statistical analysis is done in a R, and how to perform some of the most widely used methods.

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.

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ONLINE COURSE - Model-based multivariate analysis of abundance data using R (MBMV03)
2021-02-01 - 2021-02-12 | Delivered remotely (Australia), Australia
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, February 1st, 2021

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
This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework. In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and use of species traits as predictors to explain interspecific variation in environmental response. These techniques are more interpretable than alternatives, have better statistical properties, and can be used to address new problems, such as the prediction of a species’ spatial distribution from its traits alone.

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 – Australian Eastern Daylight Time – 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).

Intended Audiences
PhD students, research postgraduates, and practicing academics as well as persons in industry working with multivariate data, especially when recorded as presence/absences or some measure of abundance (counts, biomass, % cover, etc).

Venue
Delivered remotely

Course Information
Availability – 40 places
Duration – 10 daysContact hours – Approx. 30 hoursECT’s – Equal to 3 ECT’sLanguage – English
Other payment options are available please email oliverhooker@prstatistics.com

Teaching Format

A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
An understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Previous experience with data analysis using R is required. 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.
https://cran.r-project.org/

Download RStudio

It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Coming soon..

Assumed computer background
Coming soon..

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

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

There will additional Q and A support for people who can follow during real time – this will be from 21:30 to 22:00 EDT
WEEK 1
Monday 1st – Classes from 10:00 to 13:00 EDTRevision of key “Stat 101” messages
Tuesday 2nd – Classes from 10:00 to 13:00 EDTRevision of (univariate) regression analysis: the linear model, generalised linear model.Main packages: lme4.
Wednesday 3rd – Classes from 10:00 to 13:00 EDTLinear mixed models, the parametric bootstrap, permutation tests and the bootstrap.Main packages: lme4, mvabund.
Thursday 4th – Classes from 10:00 to 13:00 EDTModel selection, classical multivariate analysis.Main packages: glmnet.
Friday 5th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: hierarchical models, key properties, hypothesis testing.Main packages: mvabund.
WEEK 2
Monday 8th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: design-based inference for dependent data, indicator species.Main packages: mvabund.
Tuesday 9th – Classes from 10:00 to 13:00 EDTCompositional data, explaining cross-species patterns using traits.Main packages: mvabund.
Wednesday 10th – Classes from 10:00 to 13:00 EDTClassifying species based on environmental response, predictive modelsMain packages: Speciesmix, mvabund, lme4.
Thursday 11th – Classes from 10:00 to 13:00 EDTModel-based ordination and inferenceMain packages: gllvm.
Friday 12th – Classes from 10:00 to 13:00 EDTInferring interactions form co-occurrence dataMain packages: gllvm, ecoCopula.

Course Instructor

Dr. Antoine Becker-Scarpitta
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.

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info@website.com

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ONLINE COURSE - Advanced Ecological Niche Modelling Using R (ANMR01)
2021-01-25 - 2021-01-29 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, January 25th, 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 – UTC+2 – 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
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.
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;
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 graphic(http://www.r-project.org/).

Assumed quantitative knowledge
A basic understanding of ecological niche models and biogeography in general is required.

Assumed computer background
Experience implementing ecological niche models using R is desirable.

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

Teaches

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

Monday 25th
Classes from 09:30 to 17:30

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

Tuesday 26th
Classes from 09:30 to 17:30

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

Wednesday 27th
Classes from 09:30 to 17:30

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

Thursday 28th
Classes from 09:30 to 17:30

MESS practice with Biomod2 package.
VirtualSpecies SDMvspecies packages.
MIGCLIM practice.

Friday 29th

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 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 - Bayesian hierarchical modelling using R (IBHM05)
2020-12-04 - 2020-12-11 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Friday, December 4th, 2020

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
This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.
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 – GMT – 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).

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 Information
Time zone – GMT
Availability – 20 places
Duration – 3 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 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.
All sessions will be video recorded and made available to all attendees as soon as possible.
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.
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 regression methods and generalised linear models.
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/personal computer with a working version or R, RStudio, JAGS and stan installed. All are supported by both PC and MAC and can be downloaded for free by following these links.
https://cran.r-project.org/

Download RStudio

http://mcmc-jags.sourceforge.nethttp://mc-stan.org/
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Coming soon..

Assumed computer background
Coming soon..

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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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

Day 1
Approx 8 hours
Module 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models

Friday 27th November
Classes from 09:30 to 17:30
Module 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models

Friday 4th December
Classes from 09:30 to 17:30
Module 5: Hierarchical models vs mixed effects modelsModule 6: Multivariate and multi-layer hierarchical modelsPractical: Advanced examples of hierarchical models

Friday 11th December
Classes from 09:30 to 17:30
Module 7: Shrinkage and variable selectionModule 8: Hierarchical models and partial poolingPractical: Shrinkage modelling

Course Instructor

Dr. Antoine Becker-Scarpitta
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.

ONLINE COURSE - Python for data science, machine learning, and scientific computing (PDMS02)
2020-05-04 - 2020-05-08 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, May 4th, 2020

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. This course provides both a general introduction to programming with Python and a comprehensive introduction to using Python for data science, machine learning, and scientific computing. The major topics that we will cover include the following: the fundamentals of general purpose programming in Python; using Jupyter notebooks as a reproducible interactive Python programming environment; numerical computing using numpy; data processing and manipulations using pandas; data visualization using matplotlib, seaborn, ggplot, bokeh, altair, etc; symbolic mathematics using sympy; data science and machine learning using scikit-learn, keras, and tensorflow; Bayesian modelling using PyMC3 and PyStan; high performance computing with Cython, Numba, IPyParallel, Dask. Overall, this course aims to provide a solid introduction to Python generally as a programming language, and to its principal tools for doing data science, machine learning, and scientific computing. (Note that this course will focus on Python 3 exclusively given that Python 2 has now reached it end of life).

Intended Audiences
This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially how Python can be used for data science, broadly defined. Python and Python based data science is applicable to academic research in all fields of science and engineering as well as data intensive industries and services such as finance, pharmaceuticals, healthcare, IT, and manufacturing.

Venue
Delivered remotely

Course Details
Availability – 15 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some lecture style presentation, i.e., using slides, introducing and explaining key concepts. However, even in these cases, the topics being covered will include practical worked examples that will work through together.

Assumed quantitative knowledge
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss numerical computing, symbolic maths, and statistics and machine learning. However, expertise and proficiency with these concepts are not necessary. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics or mathematics can be assumed to have sufficient familiarity with these concepts.

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 should bring a laptop computer with Python (version 3) and the Python packages that we will use (such as numpy, pandas, sympy, etc) installed. All the required software is free and open source and is available on Windows, MacOs, and Linux. Instructions on how to install and configure all the software will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.

Download Python

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 4th
Classes from 09:30 to 17:30
Topic 1: The What and Why of Python. In order to provide some general background and context, we will describe Python where came from, what its major design principles and intended use was originally, and where and how it is now currently used. We will see that Python is now extremely widely used, especially in powering the web, in data science and machine learning, and system level programming. Here, we also compare and contrast Python and R, given that both are extremely widely used in data science.
Topic 2: 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 mention and briefly describe Jupyter notebooks, which are widely used for scientific applications of Python, and are an excellent tool for doing reproducible interactive work. We will cover Jupyter more extensively starting on Day 3. Also as part of this topic, we will describe how to use virtual environments and package installers such as pip and conda.
Topic 3: Introduction to Python: 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 4: Introduction to Python: Programming. 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 loops. We will then cover conditionals and functions.
Tuesday 5th
Classes from 09:30 to 17:30
Topic 5: 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, and also how to write our own modules and packages.
Topic 6: 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”).
Topic 7: 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.
Topic 8: 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 9: Other Python programming features. In this section, we will cover some important features of Python not yet covered. These include exception handling, list and dictionary comprehensions, itertools, advanced collection types including defaultdict, anonymous functions, decorators, etc.
Wednesday 6th
Classes from 09:30 to 17:30
Topic 10: Jupyter notebooks and Jupyterlab. Although we have already introduced Jupyter notebooks, here we will explore them properly. Jupyter notebooks are reproducible and interactive computing environment that support numerous programming languages, although Python remains the principal language used in Jupyter notebooks. Here, we’ll explore their major features and how they can be shared easily using GitHub and Binder.
Topic 11: 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 of 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. In this section, we will also consider a set of other visualization libraries for Python. These include plotly, bokeh, and altair.
Topic 12: 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 13: 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 package, which provides linear and generalized linear models as well as many other widely used statistical models. We will also introduce the scikit-learn package, which we will more widely use on Day 4, and use it for regression and classification analysis.
Thursday 7th
Classes from 09:30 to 17:30
Topic 14: Machine learning. Python is arguably the most widely used language for machine learning. In this section, we will explore some of the major Python machine learning tools that are part of the scikit-learn package. This section continues our coverage of this package that began in Topic 12 on Day 3. Here, we will cover machine learning tools such as support vector machines, decision trees, random forests, k-means clustering, dimensionality reduction, model evaluation, and cross-validation.
Topic 15: Neural networks and deep learning. A popular subfield of machine learning involves the use of artificial neural networks and deep learning methods. In this section, we will explore neural networks and deep learning using the keras library, which is a high level interface to neural network and deep learning libraries such as Tensorflow, Theano, or the Microsoft Cognitive Toolkit (CNTK). Examples that we will consider here include image classification and other classification problems taken from, for example, the UCI Machine Learning Repository.
Friday 8th
Classes from 09:30 to 16:00
Topic 16: Bayesian models. Two probabilistic programming languages for Bayesian modelling in Python are PyMC3 and PyStan. PyMC3 is a Python native probabilistic programming language, while PyStan is the Python interface to the Stan programming language, which is also very widely used in R. Both PyMC3 and PyStan are extremely powerful tools and can implement arbitrary probabilistic models. Here, we will not have time to explore either in depth, but will be able to work through a number of nontrivial examples, which will illustrate the general feature and usage of both languages.
Topic 17: High performance Python. The final topic that we will consider in this course is high performance computing with Python. While many of the tools that we considered above extremely quickly because they interface with compiled code written in C/C++ or Fortran, Python itself is a high level dynamically typed and interpreted programming language. As such, native Python code does not execute as fast as compiled languages such as C/C++ or Fortran. However, it is possible to achieve compiled language speeds in Python by compiling Python code. Here, we will consider Cython and Numba, both of which allow us achieve C/C++ speeds in Python with minimal extensions to our code. Also, in this section, we will consider parallelization in Python, in particular using IPyParallel and Dask, both of which allow easy parallel and distributed processing using Python.

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.

IN HOUSE Introduction to spatial analysis using R (INHO05)
2018-11-12 - 2018-11-16
ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live

Event Date
Monday, January 25th, 2022

Course Format
This is an ‘IN HOUSE COURSE’ – the instructor will be delivering lectures and coaching attendees on site.
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

Course overview:
This is a private ‘in house’ course being delivered on site. If you would be interested in organising an in house course at your institute or place of work please contact oliverhooker@prstatistics.com to see if we can cover your desired subject

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

IN HOUSE Introduction to R for biologists (INHO04)
2018-10-23 - 2018-10-25
