BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20251020 DTEND;VALUE=DATE:20251031 DTSTAMP:20260419T015954 CREATED:20231213T115657Z LAST-MODIFIED:20250205T150304Z UID:10000364-1760918400-1761868799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Path analysis\, structural equations and causal inference for biologists (PSCB03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 20th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Eastern Daylight Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n This course\, based primarily on my 2016 book\, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests\, why (andwhen) they are justified\, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. Thepractical work will be based on R and RStudio. Students will receive R script\, datasets\, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course\, but not essential. \nBy the end of the course\, participants should be able to: Understand the logical relationships between d-separation\, data\, and causal hypotheses. Know when to use piecewise SEM\, when to use covariance- based SEM\, and the advantages/disadvantages and assumptions of each Be able to construct\, test\, and interpret measurement models involving latent variables Be able to construct and identify equivalent models Be able to incorporate nested or mixed models\, multigroup models\, and non-normal distributions into SEM \nParticipants are encouraged to bring their own data\, as there will be opportunities throughout the course to plan\, analyze\, and receive feedback on structural equation models. \n \n \n \n \n \n Intended Audiences\n Scientists generally\, and ecologists specifically\, who want to test hypotheses concerning cause-and-effect relationships involving several variables\, especially involving observational data. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 7 x 1/2 days \nContact hours – Approx. 25 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course involves a mixture of theory and practical work. Data and analytical approaches will be presented in a lecture format to explain key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \n \n \n \n \n \n Assumed quantative knowledge\n Experience in using R and RStudio for statistical analysis. A basic understanding of statistical inference and regression methods. A familiarity of more advanced regression models (mixed models\, generalized linear models) is an asset but is not essential. \n \n \n \n \n \n Assumed computer background\n Proficiency with R programming language\, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g.\, lm()).\n \n \n \n \n \n Equipment and software requirements\n A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. A full list of required packages will be made available to participants prior to the course. \nhttps://cran.r-project.org/Download RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations 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 \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 20th\n Day 1 08:30 – 12:00Causal inference using experiments vs. observations (1h) Randomised experiments are the gold standard Limitations on randomised experiments The logic of controlled experiments Limitations of controlled experiments Physical control vs. observational control DAGs\, d-separation and data (2h) Translating from the language of causality to the language of statistics Directed acyclic graphs (DAGS) and d-separation D-separation and statistical conditioning The difference between experimental control and statistical conditioning The logic of causal inference using d-separation \n \n \n \n \n \n Tuesday 21st\n Day 2 08:30 – 12:00Path analysis using piecewise structural equation modelling (1h30) D-separation basis sets of a DAG The steps in conducting a piecewise SEM Rejecting or provisionally accepting your path model Path coefficients as measures of direct causal effect Decomposing causal effects \nPractical work (2h) \n \n \n \n \n \n Wednesday 22nd\n Day 3 8:30 – 12:00Path analysis using piecewiseSEM (2h30) The piecewiseSEM library in R \nPractical work (1h) \n  \n \n \n \n \n \n Thursday 23rd\n Day 4 08:30 – 12:00Equivalent models and AIC statistics (2h) Statistical power in SEM Provisionally accepting a causal hypothesis What is a “d-separation equivalent” DAG Rules for identifying equivalent models AIC statistic to compare between non-equivalent models How to interpret AIC statistics \nPractical work (1h30) \n \n \n \n \n \n Friday 24th\n Day 5 08:30 – 12:00Covariance-based path analysis (2h) Translating the DAG into “structural equations” The model-predicted covariance matrix An intuitive explanation of maximum likelihood estimation Estimating the free parameters via ML The concept of “degrees of freedom” The ML chi-squared statistic of model fit Rejecting (or not) your SE model \nCovariance-based path analysis using lavaan (1h30) \n \n \n \n \n \n Monday 27th\n Day 6 08:30 – 11:30 \nLatent variables and measurement models (3h) Removing latent variables from a DAG DAGs and MAGs DAG.to.MAG() function When you can’t remove a latent: measurement models Measurement models and ML estimation Fixing the scale of a latent variable Measurement models and minimum degrees of freedom Measurement models in lavaan Empirical example: measuring soil fertility \n \n \n \n \n \n Tuesday 28th\n Day 7 08:30 – 12:00 \nPractical using measurement models (1h) \nThe full structural equation model (2h30) Model identification: structural and empirical Composite variables and composite latents Consequences and solutions for small sample sizes Consequences and solutions for non-normal data Measures of approximate fit Missing data Reporting results in publications \n \n \n \n \n \n Wednesday 29th\n Day 8 08:30 – 12:00Multigroup models (2h) What is causal heterogeneity? The concept of nested models How to fit multigroup models in lavaan \nPractical: putting everything together (1h30) \n \n \n \n \n \n Thursday 30th\n Day 9 9:00 – 12:00Practical and group presentations of results \n \n \n \n \n \n \n \n \n Bill Shipley\n \n Bill Shipley is an experienced researcher and  \nteacher in plant ecology and statistical ecology.  He has published four scientific monographs and over 170 peer-reviewed papers. URL:https://prstats.preprodw.com/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/03/donna-ruiz-Pe_SZd-oA_0-unsplash-scaled.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250512 DTEND;VALUE=DATE:20250517 DTSTAMP:20260419T015954 CREATED:20230906T131146Z LAST-MODIFIED:20241120T125945Z UID:10000351-1747008000-1747439999@prstats.preprodw.com SUMMARY:ONLINE COURSE - Movement Ecology Using R(MOVE07) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, May 12th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About This Course\n The course will cover the concepts\, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa\, from micro-organisms to vertebrates\, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology)\, physics and mathematics/statistics\, to be able to identify for any specific research question the most appropriate study species\, logging technology (incl. attachment methods)\, and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’\, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts. \n \n \n \n \n \n Intended Audiences\n Research postgraduates\, practicing academics and primary investigators in ecology and management and environmental professionals in government and industry. The course will also be of interest to researchers in geography\, mathematics and computer science working on movement analyses. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 5 days \nContact hours – Approx. 37 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of trigonometry\, basic knowledge of calculus; basic knowledge of physics as relevant for biological systems. \n \n \n \n \n \n Assumed computer background\n Good familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM); generate simple exploratory and diagnostic plots. Knowledge of more advanced models\, such as mixed models\, will be helpful\, as will a basic recollection of mathematical analysis. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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. \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 12th\n Classes from 12:00 to 20:30 GMT+1 \nDAY 1Movement FundamentalsConceptual component: Introduction to movement ecology\, movement and behaviour\, spatial and movement path analysis.Practical component: Movement path analysis I – from steps and turns to movement path segmentation; Movement path analysis II – movement modes (home rage\, dispersal\, migration\, nomadism) and the squared displacement method. \nAttendees will be guided through practicals by both instructors to ensure you get the most from their experience. \n \n \n \n \n \n Tuesday 13th\n Classes from 12:00 – 20:30 GMT+1 \nDAY 2Home Range AnalysisConceptual component: Ecological definitions and interpretations of home ranges\, home range estimation\, comparisons between estimators and the question-driven approach.Practical component: Utilization distribution; comparison of contrasting kernel home range estimation methods\, isopleth creation\, core area & home range overlap. \nAttendees will be guided through practicals by both instructors to ensure you get the most from their experience. \n \n \n \n \n \n Wednesday 14th\n Classes from 12:00 – 20:30 GMT+1 \nDAY 3Dynamic Interactions and Temporal Metrics of MovementConceptual component: Movements of interacting animals – static and dynamic interactions; scales of movement – first-passage and residence time analysis.Practical component: Static and dynamic interaction indices; estimation of first-passage and residence time metrics. \nAttendees will be guided through practicals by both instructors to ensure you get the most from their experience. \n \n \n \n \n \n Thursday 15th\n Classes from 12:00 – 20:30 GMT+1 \nDAY 4Introduction to Resource Selection\, and Effects of ScaleConceptual component: Theories of resource and habitat selection\, history of approaches\, and current methodologies and caveats including definitions of availability and scale effects for RSF and other movement metricsPractical component: Data projections and R as a GIS; Scale-integrated models of movement\, availability sampling\, and RSF estimation and interpretation. \nAttendees will be guided through practicals by both instructors to ensure you get the most from their experience. \n \n \n \n \n \n Friday 16th\n Classes from 12:00 – 20:30 GMT+1 \nDAY 5Step-Selection Functions and Instantaneous AvailabilityConceptual component: Introduction to step selection\, decision-making processes\, null and alternative models for definitions of availability within SSF\, movement-integrated step-selection analysisPractical component: Creation of available step data\, estimation of SSF using multiple packages and approaches\, simulation of utilization and occurrence distributions. \nAttendees will be guided through practicals by both instructors to ensure you get the most from their experience. \n \n \n \n \n \n \n Course Instructor\n \nProf. Luca Borger\nComing Soon \n \n \n \n \n \n Course Instructor\n \nDr. Garrett Street\nComing Soon URL:https://prstats.preprodw.com/course/movement-ecology-using-rmove07/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MOVE04R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T015954 CREATED:20220224T232008Z LAST-MODIFIED:20230727T123514Z UID:10000398-1746403200-1746575999@prstats.preprodw.com SUMMARY:Introduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSCPR) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 05:05:2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n \nAttendees 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. \n\n\nDownload Python \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\nDay 1 – approx. 6 hours \nTopic 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. \nTopic 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. \nDay 2 – approx. 6 hours \nTopic 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. \nTopic 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. \nTopic 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. \nTopic 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 \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website \n\n\nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pyscpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240522 DTEND;VALUE=DATE:20240524 DTSTAMP:20260419T015954 CREATED:20220302T121123Z LAST-MODIFIED:20240229T000503Z UID:10000401-1716336000-1716508799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Adapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ05) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, May 22nd\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis 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. \nTIME ZONE\nTIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About This Course\n R statistical software is becoming increasingly popular for spatial analysis and mapping. This is partially due to a large number of R packages devoted to applying various spatial methods. These packages\, however\, are being revised\, updated\, or even superseded to allow for better performance\, simpler user interface\, or expanded capabilities. Substantial recent changes in R spatial packages include developing the ‘sf’ package as a successor of ‘sp’\, creation of `terra` as a successor of `raster`\, and establishing the `stars` package. Additionally\, all of these packages were affected by the recent major updates of the PROJ library. In this course\, we will learn to use key packages for the analysis of spatial data\, both vector (‘sf’) and raster (‘terra’)\, and see how they differ from their older counterparts\, ‘sp’ and ‘raster’. Another important aspect of the course will be to understood spatial projections and coordinate systems\, how the recent PROJ changes affect R users\, and how to adjust to them. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind spatial analysis ecosystem in R\nKnow how packages such as sp/rgeos/rgdal/raster differ from their successors sf/terra/star\nBe able to switch from using packages such as sp/rgeos/rgdal/raster to sf/terra/stars\nUnderstood the basic concepts behind spatial projections\, and how PROJ.7 differs from PROJ4\nKnow how to deal with coordinate reference systems in R\nHave the confidence to switch from PROJ4 to PROJ7 (i.e.\, for instance\, adjusting old scripts based on PROJ4)?\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to spatial data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R for spatial data analysis\nCurrent R users wanting to update your knowledge\, including switch from using `sp` to `sf`\, and from `raster` to `terra`\nThe course is designed for intermediate R users interested in understanding modern tools for spatial data analysis in R and R beginners who have prior experience with geographic data and other spatial software.\n \n \n \n \n \n Course Details\n Time zone – Central Europe Standard Time \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be a mixture of theoretical and practical. Each concept will be first described and explained\, and next the attendees will exercise the topics using provided data sets. \n \n \n \n \n \n Assumed quantitative knowledge\n Understanding basic GIS concepts\, such as spatial vector\, spatial raster\, coordinate reference systems would be beneficial\, but is not necessary. \n \n \n \n \n \n Assumed computer background\n Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. The experience of using some basic R spatial packages\, such as sp or raster would be beneficial. \nHowever\, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types\, and some programming experience\, the course may also be appropriate for you. \n \n \n \n \n \n Equipment and software requirements\n \nA 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. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).  \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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. \n​ \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Wednesday 22nd\n Classes from 09:00 to 17:00Overview of spatial analysis ecosystem in R• available R packages for spatial analysis in R• how do R packages represent spatial objects\, and how are they connected with each other• importance of using the more recent R spatial packages\, such as ‘sf’ or ‘terra’• main concepts behind map projections (geoids\, datums\, geographic/projected coordinates\, types of projections\, etc.)• implementation of these concepts in the PROJ library (used by most R spatial packages)• differences between PROJ.4 and its newer versions (e.g. PROJ.7)Spatial vector data analysis in R• spatial vector data processing & analysis in R• read/write/and visualize spatial vector data• differences between ‘sp’/’rgdal’/’rgeos’ and ‘sf’• moving from ‘sp’ to ‘sf’ for spatial vector data processing & analysis• spherical geometry: how this concept was recently implemented in sf\, and what is an impact of this implementation \n \n \n \n \n \n Thursday 23rd\n Classes from 09:00 to 17:00Spatial raster data analysis in R• spatial raster data processing & analysis in R• read/write/and visualize spatial raster data• differences between ‘raster’ and ‘stars’/’terra’• moving from ‘raster’ to ‘terra’ for spatial raster data processing & analysis• short overview of package ‘stars’Coordinate reference systems• how to switch from PROJ.4 to PROJ.7 in R• open session: questions from the participants \n \n \n \n \n \n \n \n \n Jakub Nowosad\n Works at: Adam Mickiewicz University \n Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create\, collaborate\, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book. \nResearchGateGoogleScholarORCIDLinkedInGitHub \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ\n\n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ URL:https://prstats.preprodw.com/course/adapting-to-the-recent-changes-in-r-spatial-packages-sf-terra-proj-library-proj05/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PROJ02R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240319 DTEND;VALUE=DATE:20240323 DTSTAMP:20260419T015954 CREATED:20231129T180550Z LAST-MODIFIED:20240223T134431Z UID:10000440-1710806400-1711151999@prstats.preprodw.com SUMMARY:ONLINE COURSE - An Introduction to Spatial Eco-Phylogenetics and Comparative Methods (SECM01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, March 19th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis 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. \nCOURSE PROGRAM\nTIME ZONE – CET (Central European Time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n \nIn this course we introduce phylogenetic analyses in a spatial context. Phylogenetic analyses often imply a high number of species for which phylogenetic information is unavailable\, hence we begin by providing an overview on modern techniques to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover the most popular analyses in the spatial phylogenetics discipline (day 2)\, with particular focus on the canonical analysis of neo- and paleo-endemism (CANAPE). The second part of the course will be devoted to integrating phylogenetic information into models of geographic distribution of species (day 3)\, followed by an overview of recent advances to improve ecological forecasts using phylogenetic mixed models in a Bayesian framework (day 4).  \n\nBy the end of the course\, participants should: \n\nKnow how to expand incomplete phylogenies based on taxonomic information and customizing simulation parameters for optimal expansion.\nUnderstand the metrics and concepts used in spatial phylogenetics (i.e. phylogenetic alpha and beta diversity\, phylogenetic endemism)\, interpret them critically\, and assess pros and cons of analytical techniques.\nCalculate phylogenetic predictors that can be included as covariates in Species Distribution or Niche Models.\nUnderstand and implement the phylogenetic mixed model (PMM) and translate its predictions into a spatial context.\n\n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to introduce into spatial phylogenetics and comparative analyses (in general and within a spatial context in particular) \n \n \n \n \n \n Course Details\n Venue – Delivered remotelyAvailability – 30 placesDuration – 5 daysContact hours – Approx. 35 hoursECT’s – Equal to 3 ECT’sLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared with the attendees. \n \n \n \n \n \n Assumed quantative knowledge\n \nWe will assume general familiarity with the very basics of statistics (e.g. summary statistics\, distributions). As this is an introductory course\, no phylogenetic background is required. \n\n \n \n \n \n \n Assumed computer background\n We will assume general familiarity with R elementary operations (e.g. package sourcing\, data importing and exporting\, object indexing) and some familiarity with programming in R (writing code). \n \n \n \n \n \n Equipment and software requirements\n \nA 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. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\n\n\nCancellations 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. \n\n\n\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n\n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 19th\n Classes from 8:00 to 13:00 CET \nDAY 1 \nExpansion of molecular trees using taxonomic information and fundamental metrics of phylogenetic structure \n\nSoftware for tree expansion exercises; randtip\, PhyloMaker\nAn overview of the fundamental metrics of phylogenetic structure. Null models.\n\n  \n \n \n \n \n \n Wednesday 20th\n Classes from 8:00 to 13:00 CET \nDAY 2 \nSpatial Phylogenetics \n\nCanonical analysis of neo- and paleo- endemism. Metrics\, rationale\, workflow\, and implementation.\n\n \n \n \n \n \n Thursday 21st\n Classes from 8:00 to 13:00 CET \nDAY 3 \nPhylogenetic Species Distribution Models \n\nPutting phylogenies in the geography: the imprints of evolutionary relationships in distribution models.\nCombining phylogenies with co-occurrence to infer spatial phylogenetic predictors.\nFitting\, evaluating and interpreting Phylogenetic-SDMs.\n\n \n \n \n \n \n Friday 22nd\n Classes from 8:00 to 13:00 CET \nDAY 4 \nBeyond PGLS – Bayes and more \n\nMost common phylogenetic modelling approaches: PGLS\nPGLMM\nThe phylogenetic mixed model (PMM) in a Bayesian framework\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael Molina Venegas \nThe scientific career of Rafael Molina Venegas revolves around three research lines pertaining to (1) the ecological and evolutionary mechanisms that jointly shape species assemblages at the community and macroecological scales\, (2) the development\, improvement\, and assessment of phylogenetic methods\, and (3) the links between biodiversity and human well-being. While these lines represent clearly differentiated research interests\, phylogenetics is a cross-cutting background for all of them. Considering that plants are his true passion in science\, he defines himself as a Phylogenetic Plant Ecologist. I personal page can be found here \nResearchGateGoogleScholar \n \n \n \n \n \n Course Instructor\n \nDr. Morales Castilla Ignacio \nDr. Morales-Castilla is a biogeographer and macroecologist interested in the spatial-temporal distribution of biodiversity. His research program aims to: (1) disentangle the relative roles of evolution and ecology as drivers of community structure\, (2) understand how different aspects of the species’ niches are evolutionarily conserved and\, (3) enhance models of biotic interactions and/or species distributions by integrating phylogenetic\, functional and geographic information. You can check his publication record at the links provided above. You can find hiss homepage here \nResearchGateGoogleScholarORCIDGitHub URL:https://prstats.preprodw.com/course/an-introduction-to-spatial-eco-phylogenetics-and-comparative-methods-secm01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240109 DTEND;VALUE=DATE:20240112 DTSTAMP:20260419T015954 CREATED:20220614T145529Z LAST-MODIFIED:20231223T114438Z UID:10000412-1704758400-1705017599@prstats.preprodw.com SUMMARY:ONLINE COURSE – Model selection and model simplification (MSMS04) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, January 9th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis 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. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n​\n \n \n \n \n \n \n \n \n \n \n About This Course\n This three day course covers the important and general topics of statistical model building\, model evaluation\, model selection\, model comparison\, model simplification\, and model averaging. These topics are vitally important to almost every type of statistical analysis\, yet these topics are often poorly or incompletely understood. We begin by considering the fundamental issue of how to measure model fit and a model’s predictive performance\, and discuss a wide range of other major model fit measurement concepts like likelihood\, log likelihood\, deviance\, residual sums of squares etc. We then turn to nested model comparison\, particularly in general and generalized linear models\, and their mixed effects counterparts. We then consider the key concept of out-of-sample predictive performance\, and discuss over-fitting or how excellent fits to the observed data can lead to very poor generalization performance. As part of this discussion of out-of-sample generalization\, we introduce leave-one-out cross-validation and Akaike Information Criterion (AIC). We then cover general concepts and methods related to variable selection\, including stepwise regression\, ridge regression\, Lasso\, and elastic nets. Following this\, we turn to model averaging\, which is an arguably always preferable alternative to model selection. Finally\, we cover Bayesian methods of model comparison. Here\, we describe how Bayesian methods allow us to easily compare completely distinct statistical models using a common metric. We also describe how Bayesian methods allow us to fit all the candidate models of potential interest\, including cases were traditional methods fail. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Venue\n Delivered remotely. \n \n \n \n \n \n Course Details\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n \n \n \n \n \n Assumed computer background\n \n \n \n \n \n \n Equipment and software requirements\n \nA 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. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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. \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 9th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: Measuring model fit. In order to introduce the general topic of model evaluation\, selection\, comparison\, etc.\, it is necessary to understand the fundamental issue of how we measure model fit. Here\, the concept of conditional probability of the observed data\, or of future data\, is of vital importance. This is intimately related\, though distinct\, to concept of likelihood and the likelihood function\, which is in turn related to the concept of the log likelihood or deviance of a model. Here\, we also show how these concepts are related to concepts of residual sums of squares\, root mean square error (rmse)\, and deviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested model comparison in general linear models\, generalized linear models\, and their mixed effects (multilevel) counterparts. First\, we precisely define what is meant by a nested model. Then we show how nested model comparison can be accomplished in general linear models with F tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear models\, and mixed effects models\, we can accomplish nested model comparison using deviance based chi-square tests via Wilks’s theorem. \n \n \n \n \n \n Wednesday 10th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nTopic 3: Out of sample predictive performance: cross validation and information criteria. In the previous sections\, the focus was largely on how well a model fits or predicts the observed data. For reasons that will be discussed in this section\, related to the concept of overfitting\, this can be a misleading and possibly even meaningless means of model evaluation. Here\, we describe how to measure out of sample predictive performance\, which measures how well a model can generalize to new data. This is arguably the gold-standard for evaluating any statistical models. A practical means to measure out of sample predictive performance is cross-validation\, especially leave-one-out cross-validation. Leave-one-out cross-validation can\, in relatively simple models\, be approximated by Akaike Information Criterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how to interpret AIC values\, and describe other related information criteria\, some of which will be used in more detail in later sections. \nTopic 4: Variable selection. Variable selection is a type of nested model comparison. It is also one of the most widely used model selection methods\, and variable selection of some kind is almost always done routinely in all data analysis. Although we will also have discussed variable selection as part of Topic 2 above\, we discuss the topic in more detail here. In particular\, we cover stepwise regression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets. \n \n \n \n \n \n Thursday 11th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 5: Model averaging. Rather than selecting one model from a set of candidates\, it is arguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using information criteria. \nTopic 6: Bayesian model comparison methods. Bayesian methods afford much greater flexibility and extensibility for model building than traditional methods. They also allow us to easily directly compare completely unrelated statistical models of the same data using information criteria such as WAIC and LOOIC. Here\, we will also discuss how Bayesian methods allow us to fit all models of potential interest to us\, including cases where model fitting is computationally intractable using traditional methods (e.g.\, where optimization convergence fails). This allows us therefore to consider all models of potential interest\, rather than just focusing on a limited subset where the traditional fitting algorithms succeed. \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGateGoogleScholarORCIDGitHub URL:https://prstats.preprodw.com/course/online-course-model-selection-and-model-simplification-msms04/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MSMS03.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230313 DTEND;VALUE=DATE:20230318 DTSTAMP:20260419T015954 CREATED:20250203T111240Z LAST-MODIFIED:20250203T111246Z UID:10000469-1678665600-1679097599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, May 12th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded\n \n \n \n \n \n \n \n \n \n \n About This Course\n In this three-day course\, we introduce species distribution models (SDMs) and ways toincorporate phylogenetic information into single species models using R. We begin byproviding an overview on the use of SDMs as a central tool for ecologists and evolutionarybiologists\, review and implement common SDM approaches and introduce hybrid models\,which use the information in functional traits to complement the models. We then justifythe rationale for using phylogenetic information in absence of functional trait data andshow how to incorporate phylogenetic information in SDMs (day 1). We review examplesof practical implementation of PSDMs to both present and future climate scenarios (day 2).Finally\, we overview more advanced approaches of incorporating phylogenies into models(the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatialcontext (day 3). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to improve/complement their use of SpeciesDistribution Models using phylogenies. \n \n \n \n \n \n Course Details\n Venue – Delivered remotelyAvailability – 20 placesDuration – 3 daysContact hours – Approx. 18 hoursECT’s – Equal to 2 ECT’sLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:00pm-16:00pm \nAll sessions will be video recorded and made available to all attendees. \nAttendees in different time zones will be able to join into some of these live broadcasts\, even if all of them are not convenient times. \nBy joining any live sessions that are possible\, this will allow attendees to benefit Fromm asking questions and having discussions\, rather than just watching prerecorded sessions. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared with the attendees. \n \n \n \n \n \n Assumed quantative knowledge\n We will assume general familiarity with the very basics of statistics (e.g. summarystatistics\, distributions). \n \n \n \n \n \n Assumed computer background\n We will assume general familiarity with R elementary operations (e.g. package sourcing\,data importing and exporting\, object indexing) and some familiarity with programming inR (writing code). \n \n \n \n \n \n Equipment and software requirements\n \nA 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. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 12th\n Classes from 8:00 to 16:00 \nIntroduction to species distribution models: rationale\, algorithms\, validation and applications. \n• Working with SDMs. Implementing SDMs in R.• Hybrid-SDMs or how to incorporate functional information into the models.• What to do in absence of functional trait data? The rationale for using the latent information in phylogenies instead.• The phylogenetic predictor. \n \n \n \n \n \n Tuesday 13th\n Classes form 08:00 – 16:00 \nPutting phylogenies in the geography: how to combine phylogenies with speciesdistribution models in R. \n• Phylogenetic information can improve both present and future predictions ofspecies distributions.• Projecting phyloSDMs across space and time in R.• When and why phylogenies can and can’t improve models. \n \n \n \n \n \n Wednesday 14th\n Classes form 08:00 – 16:00 \nPhylogenies also improve models for the temporal distribution of species. \n\nThe Bayesian Phylogenetic Mixed Model\nExamples of implementation of PMMs and extrapolating their predictions to thegeography in R.\n\n \n \n \n \n \n \n Course Instructor\nDr. Morales Castilla Ignacio \n  URL:https://prstats.preprodw.com/course/phylogenetic-species-distribution-modelling-using-r-psdm01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230313 DTEND;VALUE=DATE:20230318 DTSTAMP:20260419T015955 CREATED:20220303T113648Z LAST-MODIFIED:20221011T141420Z UID:10000405-1678665600-1679097599@prstats.preprodw.com SUMMARY:ONLINE COURSE -Introduction to eco-phylogenetics and comparative analyses using R (ECPH02) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, March 13th\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n In this five day course\, we provide an introduction to eco-phylogenetics and comparative analyses using R. We begin by providing an  overview on the use of phylogenies as a tool for evolutionary biologists and modern techniques to deal with large phylogenies and to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover some of the most relevant eco-phylogenetic analyses and provide examples from the community to themacro-ecological scale (day 2-3). Finally\, we introduce a diversity of classic and modern phylogenetic comparative methods to consider the historical relationship of lineages in eco-evolutionary research\, including models of trait evolution\, analysis of clade diversification and the use of phylogenies in spatial distribution models among others (day 4-5). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses. \n \n \n \n \n \n Course Details\n Venue – Delivered remotelyAvailability – 30 placesDuration – 5 daysContact hours – Approx. 35 hoursECT’s – Equal to 3 ECT’sLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:30pm-16:30pm \nAll sessions will be video recorded and made available to all attendees. \nAttendees in different time zones will be able to join into some of these live broadcasts\, even if all of them are not convenient times. \nBy joining any live sessions that are possible\, this will allow attendees to benefit Fromm asking questions and having discussions\, rather than just watching prerecorded sessions. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared with the attendees. \n \n \n \n \n \n Assumed quantative knowledge\n We will assume general familiarity with the very basics of statistics (e.g. summary statistics\, distributions). As this is an introductory course\, no phylogenetic background is required. \n \n \n \n \n \n Assumed computer background\n We will assume general familiarity with R elementary operations (e.g. package sourcing\, data importing and exporting\, object indexing) and some familiarity with programming in R (writing code). \n \n \n \n \n \n Equipment and software requirements\n Attendees of the course must use a computer with R/RStudio installed\, as well as the necessary additional R packages. Instructions on how to install the software will be provided before the start of the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/Download RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 13th\n Classes from 8:00 to 16:30 \n• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists\, phylogenetic inference (quick overview)\, phylogenies aevolutionary hypotheses. \n• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning\, resolving polytomies\, sticking species). Visualizing large phylogenies. \n• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom\, V.PhyloMaker\, SUNPLIN and randtip R package. \n  \n \n \n \n \n \n Tuesday 14th\n • Introduction to the eco-phylogenetic framework\, classical conception and posterior modifications. \n• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices\, null models\, applications to biodiversity conservation. \n• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity. \n \n \n \n \n \n Wednesday 15th\n • Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses. \n• Spatial phylogenetics. RPD\, RPE and CANEPE analysis. \n• Overview of functional trait ecology. Functional richness\, evenness and divergence.Community weighted means. \n• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology \n \n \n \n \n \n Thursday 16th\n \nThe phylogenetic comparative method\, from independent contrasts to sophisticated modelling.\nAnalyses of phylogenetic signal and models of evolution: rationale\, common- practice\, and new trends.\nCorrelated evolution and ancestral trait reconstruction.\nAnalyses of diversification\, speciation and extinction rates in a geographic context.\n \n\n \n \n \n \n \n Friday 17th\n \nThe need to account for phylogenetic relationships in models.\nMost common phylogenetic modelling approaches: PGLS\, PGLMM\, BayesianPMM.\nPutting phylogenies in the geography: how to combine phylogenies with species distribution models.\n\n \n \n \n \n \n \n Course Instructor\n \n  URL:https://prstats.preprodw.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph02/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20221018 DTEND;VALUE=DATE:20221021 DTSTAMP:20260419T015955 CREATED:20210724T164637Z LAST-MODIFIED:20221018T101649Z UID:10000345-1666051200-1666310399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bayesian Data Analysis (BADA02) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, 18th October\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n Course Details\n Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses\, many researchers do not have opportunities to learn the fundamentals of Bayesian methods\, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods\, both theoretically and practically. We will begin by teaching the fundamental concepts of Bayesian inference and Bayesian modelling\, including how Bayesian methods differ from their classical statistics counterparts\, and show how to do Bayesian data analysis in practice in R. We then provide a solid introduction to Bayesian approaches to these topics using R and the brms package. We begin by covering Bayesian approaches to linear regression. We will then proceed to Bayesian approaches to generalized linear models\, including binary logistic regression\, ordinal logistic regression\, Poisson regression\, zero-inflated models\, etc. Finally\, we will cover Bayesian approaches to multilevel and mixed effects models. Throughout this course\, we will be using\, via the brms package\, Stan based Markov Chain Monte Carlo (MCMC) methods. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science\, including the social sciences\, life sciences\, physical sciences. No prior experience or familiarity with Bayesian statistics is required. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – 30 places \nDuration – 3 days \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nIf 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. \nAt 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. \nAlthough 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. \nAll 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 \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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. \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Classes from 10:00 to 18:00 \n• 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. \n• Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such\, it can be used as a means for performing statistical inference. In this section of the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all of Bayesian data analysis is based on an application of these methods to more complex statistical models\, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases. \n• 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. \n \n \n \n \n \n Wednesday 19th\n Classes from 10:00 to 18:00 \n• Topic 4: Bayesian analysis of normal models. Statistical models based on linear and normal distribution are a mainstay of statistical analyses in general. They encompass models such as linear regression\, Pearson’s correlation\, t-tests\, ANOVA\, ANCOVA\, and so on. In this section\, we will describe how to do Bayesian analysis of normal linear models\, focusing on simple examples. One of the aims of this section is to identify some important and interesting parallels between Bayesian and classical or frequentist analyses. This shows how Bayesian and classical analyses can be seen as ultimately providing two different perspectives on the same problem. \n• Topic 5: The previous section provides a so-called analytical approach to linear and normal models. This is where we can calculate desired quantities and distributions by way of simple formulae. However\, analytical approaches to Bayesian analyses are only possible in a relatively restricted set of cases. On the other hand\, numerical methods\, specifically Markov Chain Monte Carlo (MCMC) methods can be applied to virtually any Bayesian model. In this section\, we will re-perform the analysis presented in the previous section but using MCMC methods. For this\, we will use the brms package in R that provides an exceptionally easy to use interface to Stan. \nTopic 6: Bayesian linear models. We begin by covering Bayesian linear regression. For this\, we will use the brm command from the brms package\, and we will compare and contrast the results with the standard lm command. By comparing and contrasting brm with lm we will see all the major similarities and differences between the Bayesian and classical approach to linear regression. We will\, for example\, see how Bayesian inference and model comparison works in practice and how it differs conceptually and practically from inference and model comparison in classical regression. As part of this coverage of linear models\, we will also use categorical predictor variables and explore varying intercept and varying slope linear models. \n  \n \n \n \n \n \n Thursday 20th\n Classes from 10:00 to 18:00 \n• Topic 7: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that do not always hold in practice. For example\, they assume a normal distribution of the residuals\, and assume homogeneity of variance of this distribution across all values of the predictors. In Bayesian models\, these assumptions are easily relaxed. For example\, we will see how we can easily replace the normal distribution of the residuals with a t-distribution\, which will allow for a regression model that is robust to outliers. Likewise\, we can model the variance of the residuals as being dependent on values of predictor variables. \n• Topic 8: Bayesian generalized linear models. Generalized linear models include models such as logistic regression\, including multinomial and ordinal logistic regression\, Poisson regression\, negative binomial regression\, zero-inflated models\, and other models. Again\, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. In our coverage of this topic\, we will see how powerful Bayesian methods are\, allowing us to easily extend our models in different ways in order to handle a variety of problems and to use assumptions that are most appropriate for the data being modelled. \n• Topic 9: Multilevel and mixed models. In this section\, we will cover the multilevel and mixed effects variants of the regression models\, i.e. linear\, logistic\, Poisson etc\, that we have covered so far. In general\, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups\, and so on). For this\, we use a wide range of real-world data-sets and problems\, and move between linear\, logistic\, etc.\, models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models\, and how to choose between maximal and minimal models. We will also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical problems (e.g. lack of model convergence) that beset classical approaches. \n  \n  \n \n \n \n \n \n \n \n \n Dr. Mark Andrews\n Works at: Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n Teaches:\n\nFree Introduction To Statistics Using R And Rstudio (IRRS)\nIntroduction To Generalised Linear Models Using R And Rstudio (IGLM)\nIntroduction To Mixed Models Using R And Rstudio (Immr)\nNonlinear Regression Using Generalized Additive Models Using R (GAMR)\nIntroduction To Machine Learning And Deep Learning Using R (IMDL)\nModel Selection And Model Simplification (MSMS)\nData Visualization Using Gg Plot 2 (R And Rstudio) (DVGG)\nData Wrangling Using R And Rstudio (DWRS)\nReproducible Data Science Using Rmarkdown\, Git\, R Packages\, Docker\, Make & Drake\, And Other Tools (RDRP)\nIntroduction/Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDA)\nBayesian Data Analysis (BADA)\nBayesian Approaches To Regression And Mixed Effects Models Using R And Brms (BARM)\nIntroduction To Stan For Bayesian Data Analysis (ISBD)\nIntroduction To Python (PYIN)\nIntroduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSC)\nMachine Learning And Deep Learning Using Python (PYML)\nPython For Data Science\, Machine Learning\, And Scientific Computing (PDMS)\n\nMark 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. \nResearchGate URL:https://prstats.preprodw.com/course/bayesian-data-analysis-bada02/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BADA01R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220518 DTEND;VALUE=DATE:20220520 DTSTAMP:20260419T015955 CREATED:20220303T115627Z LAST-MODIFIED:20220316T135650Z UID:10000366-1652832000-1653004799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Learning and Deep Learning Using Python (PYML03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, May 18th\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nThis 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. \nTIME ZONE\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n Python is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in machine learning and for deep learning. In this two day course\, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape\, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice\, and for this\, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems\, then look at decision trees and random forests\, then look at unsupervised learning methods\, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this\, we will using the PyTorch deep learning toolbox. Here\, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks. \n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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 \nAll 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. \n \n \n \n \n \n Assumed quantative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n We assume familiarity with using Python\, general purpose programming in Python\, and numerical programming in Python. Note that both of these topics covered comprehensively in two preceding two-day courses\, which together will provide all the computing prerequisites for this course. \n \n \n \n \n \n Equipment and software requirements\n 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. \nIf 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. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Wednesday 18th\n Classes from 10:00 to 18:00 \n• Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning\, deep learning\, big data\, data science have become widely used and celebrated in the last 10 years. However\, their definitions are relatively nebulous\, and how they related to one another and to major fields like artificial intelligence and general statistics are not simple matters. In this introduction\, we briefly overview the field of machine learning and deep learning\, discussing its major characteristics and sub-topics\, and also discuss the prominence of Python in the area. \n• Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine learning\, and is usually the first topic covered in introductions to machine learning. Here\, we will cover image classification (itself a “hello world” type problem in machine learning)\, and particularly focus on logistic regression and support vector machines (SVMs). \n• Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method\, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated\, which often leads to much improved performance. \n  \n \n \n \n \n \n Thursday 19th\n Classes from 10:00 to 18:00 \n• Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in unclassified data. Here\, we will look at two of the most widely used unsupervised techniques: k-means clustering and probabilistic mixture models. \n• Topic 5: Introducing artificial neural networks and deep learning with PyTorch. Python provides many popular libraries for artificial neural networks and deep learning. These include Keras and TensorFlow. Here\, we will use PyTorch\, which is a relatively new but increasingly high-level neural network model building and training language. These models often use graphical processing units (GPUs) for computing. Given that most personal computers don’t have adequate GPUs\, we will use Google’s Colaboratory https://colab.research.google.com/\,which is a free Python Jupyter notebook service from Google. \n• Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful\, yet relatively easy to understand\, artificial neural networks. They are also the simplest type of deep learning model. Here\, we will build and train a multilayer perceptron for a classification problem. \n• Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at image classification\, primarily due to their translation invariance\, which is highly suitable for computational vision. Here\, we use PyTorch to build and train a CNN for image classification. \n \n \n \n \n \n \n Course Instructor\n \nDr. Mark Andrews \nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/machine-learning-and-deep-learning-using-python-pyml03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYML03R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220504 DTEND;VALUE=DATE:20220506 DTSTAMP:20260419T015955 CREATED:20220218T162532Z LAST-MODIFIED:20220316T135034Z UID:10000348-1651622400-1651795199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSC03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, May 4th\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nThe 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 \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\nWednesday 4th – Classes from 10:00 to 18:00 \nTopic 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. \nTopic 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. \nThursday 5th – Classes from 10:00 to 18:00 \nTopic 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. \nTopic 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. \nTopic 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. \nTopic 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 \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website \n\n\nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pysc03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220427 DTEND;VALUE=DATE:20220429 DTSTAMP:20260419T015955 CREATED:20220224T223604Z LAST-MODIFIED:20220329T153816Z UID:10000397-1651017600-1651190399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction To Python And Programming In Python (PYIN03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, April 20th\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n \nPython 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. \n\n \n \n \n \n \n Intended Audiences\n \nThis 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. \n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:\n• 10am-12pm\n• 1pm-3pm\n• 4pm-6pm \nAll 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.\n \n \n \n \n \n Assumed quantitative knowledge\n No particular knowledge of mathematics or statistics is required.\n \n \n \n \n \n Assumed computer background\n \nNo 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. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees 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. \nIf 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. \n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\nWednesday 27th \nClasses from 10:00 to 18:00 \nTopic 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. \nTopic 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. \nTopic 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. \nThursday 28th \nClasses from 10:00 to 18:00 \nTopic 4: Programming II. Having covered iterations\, we now turn to other major programming features in Python\, specifically\, conditionals\, functions\, and exceptions. \nTopic 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. \nTopic 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. \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/introduction-to-python-and-programming-in-python-pyin03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYIN03R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20211020 DTEND;VALUE=DATE:20211023 DTSTAMP:20260419T015955 CREATED:20220303T111937Z LAST-MODIFIED:20220310T122836Z UID:10000404-1634688000-1634947199@prstats.preprodw.com SUMMARY:FREE RECORDED 1 DAY INTRO TO R AND R STUDIO (FIRR01) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, April 13th\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis 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”. \n\n\nTime \n17:00 GMT \n\n\nSpeaker \n\n\nCourse Instructor Dr. Mark Andrews \nAbout this course \nPython 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. \n\n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \nIF 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 \nWE 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 \nTHIS IS A FREE INTRODUCTORY COURSE – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE PAID COURSES IN THIS SERIES \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Venue – Delivered remotely \nTime zone – NA \nAvailability – NA \nDuration – 1 days \nContact hours – Approx. 6 hours \nECT’s – NA \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n 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. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Topic 1\n 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. \n \n \n \n \n \n Topic 2\n 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. \n \n \n \n \n \n Topic 3\n 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. \n \n \n \n \n \n \n \n \n Mark Andrews\n \n Dr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/free-1-day-intro-to-r-and-r-studio-firr01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03R.png GEO:53.1423672;-7.6920536 END:VEVENT END:VCALENDAR