BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH 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:20250325 DTEND;VALUE=DATE:20250329 DTSTAMP:20260418T225120 CREATED:20230915T120720Z LAST-MODIFIED:20250122T145633Z UID:10000429-1742860800-1743206399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Stable Isotope Mixing Models using MixSIAR and SIBER (SIMM11) This course will be delivered live DESCRIPTION:Delivered remotely (Ireland)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, March 25th\, 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 \n \n \n \n \n \n \n \n \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 – 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 Course Details\n This course will cover the concepts\, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models\, we will cover the do’s and don’ts of using SIMMs. We will then focus on the widely used package MixSIAR and SIBER packages. MixSIAR creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes\, fatty acids)\, which estimate the proportions of source (prey) contributions to a mixture (consumer). ‘MixSIAR’ is not one model\, but a framework that allows a user to create a mixing model based on their data structure and research questions\, via options for fixed/ random effects\, source data types\, priors\, and error terms. ‘MixSIAR’ incorporates several years of advances since ‘MixSIR’ and ‘SIAR’. SIBER fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Participants will be taught the advanced features of these packages\, which will enable them to produce a richer class of output. Attendees are encouraged to bring their own data sets and problems to study during the round-table discussions. \n \n \n \n \n \n Intended Audiences\n The course is aimed at biologists with a basic to moderate knowledge in R. The course is aimed at anyone (academic or industry) who research is heavily reliant on analysing stable isotope data. There is a strong association with data on food webs and trophic relationships\, but the tools learned can be applied to other systems.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data.\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. \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 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 If 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 COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 25th\n Classes from 09:30 to 17:30 \nDAY 1Basic concepts.Module 1: Introduction; why use a SIMM?Module 2: An introduction to bayesian statistics.Module 3: Differences between regression models and SIMMs.Practical: Revision on using R to load data\, create plots and fit statistical models.Round table discussion: Understanding the output from a Bayesian model. \n \n \n \n \n \n Wednesday 26th\n Classes from 09:30 to 17:30 \nDAY 2Understanding and using SIAR.Module 4: Do’s and Don’ts of using SIAR.Module 5: The statistical model behind SIAR.Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.Round table discussion: Issues when using simple SIMMs. \n \n \n \n \n \n Thursday 27th\n Classes from 09:30 to 17:30 \nDAY 3SIBER and MixSIAR.Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).Module 7: What are the differences between SIAR and MixSIAR?Practical: Using MixSIAR on real world data sets; benefits over SIAR.Round table discussion: When to use which type of SIMM. \n \n \n \n \n \n Friday 28th\n Classes from 09:30 to 17:30 \nDAY 4Advanced SIMMs.Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.Module 9: Source grouping: when and how?Module 10: Building your own SIMM with JAGS.Practical: Running advanced SIMMs with JAGS.Round table discussion: Bring your own data set. \n \n \n \n \n \n \n \n \n Andrew Jackson\n \n My research interests lie in understanding ecological systems from an evolutionary perspective. I tend to approach these questions by using computational / mathematical models to understand how the nuts and bolts of these systems work. Much of my current research focuses on understanding predator-prey interactions and how large fish use their spatial environment. My interests also extend to community ecology where the challenge is to understand how communities of organisms and species compete and interact with what is often a self-organising and stable system. \nResearch GateGoogle ScholarORCIDHomepageGitHub URL:https://prstats.preprodw.com/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SIMM08R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231204 DTEND;VALUE=DATE:20231209 DTSTAMP:20260418T225120 CREATED:20230718T154854Z LAST-MODIFIED:20231204T165620Z UID:10000430-1701648000-1702079999@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIA01) This course will be delivered live DESCRIPTION:Delivered remotely (Ireland)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, August 28th\, 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\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 – 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 This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software\, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms\, how they work\, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts. \nBy the end of this course\, students should be able to: \n\nUnderstand and implement advanced supervised learning techniques such as CNNs\, RNNs\, Transformer Models\, and Bayesian Machine Learning methods.\nUnderstand and implement advanced unsupervised learning techniques including various clustering\, dimension reduction\, and anomaly detection methods.\nApply these techniques to real-world datasets and interpret the results.\nUnderstand the underlying methods and assumptions/drawbacks of these techniques.\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – TBC \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests\, Gradient Boosting\, k-NN\, SVMs. \n \n \n \n \n \n Assumed computer background\n Good familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots. \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 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 If 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 COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 4th\n Classes from 09:30 to 17:30 GMT+1 \nDAY 1 \nDeep Dive into Supervised Learning \nWe begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs)\, exploring their architecture\, their use in image and video processing\, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing. \nIn the afternoon sessions we implement CNNs and RNNs using real data sets \nR Packages used: keras\, tensorflow \n \n \n \n \n \n Tuesday 5th\n Classes from 09:30 to 17:30 GMT+1 \nDAY 2 \nAdvanced Supervised Learning Techniques \nOn day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture\, its self-attention mechanism\, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks\, and compare it to traditional machine learning methods. \nIn the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts. \nR Packages: keras\, tensorflow\, rstan\, brms\, BART \n \n \n \n \n \n Thursday 7th\n Classes from 09:30 to 17:30 GMT+1 \nDAY 3 \nUnsupervised Learning – Clustering and Dimension Reduction \nThe third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering\, DBSCAN\, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions. \nIn the afternoon session students will explore the use of these techniques through real-world data sets. \nR Packages: cluster\, dbscan\, factoextra\, Rtsne\, umap \n \n \n \n \n \n Friday 8th\n Classes from 09:30 to 17:30 GMT+1 \nDAY 4 \nUnsupervised Learning – Anomaly Detection and Course Wrap-up \nOn the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection\, network security\, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning. \nIn the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor. \nR Packages: anomalize\, forecast\, e1071 \n \n \n \n \n \n \n \n \n Dr. Andrew Parnell\n \n Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science\, Nature Communications\, and Proceedings of the National Academy of Sciences\, and has methodological publications in journals such as Statistics and Computing\, Journal of Computational and Graphical Statistics\, The Annals of Applied Statistics\, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics\, time series modelling\, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change\, 3D printing\, and bioinformatics. \nResearch GateGoogle ScholarORCIDLinkedInGitHub URL:https://prstats.preprodw.com/course/machine-learning-with-r-intermediate-advanced-mlia01/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2023/07/hunter-harritt-Ype9sdOPdYc-unsplash-scaled.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231204 DTEND;VALUE=DATE:20231205 DTSTAMP:20260418T225120 CREATED:20240220T161332Z LAST-MODIFIED:20240709T140540Z UID:10000450-1701648000-1701734399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIAPR) DESCRIPTION:Delivered remotely (Ireland)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 Course Details\n This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software\, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms\, how they work\, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts. \nBy the end of this course\, students should be able to: \n\nUnderstand and implement advanced supervised learning techniques such as CNNs\, RNNs\, Transformer Models\, and Bayesian Machine Learning methods.\nUnderstand and implement advanced unsupervised learning techniques including various clustering\, dimension reduction\, and anomaly detection methods.\nApply these techniques to real-world datasets and interpret the results.\nUnderstand the underlying methods and assumptions/drawbacks of these techniques.\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Availability – TBC \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests\, Gradient Boosting\, k-NN\, SVMs.\n \n \n \n \n \n Assumed computer background\n Good familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots.\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 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 If 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 COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n DAY 1 \nDeep Dive into Supervised Learning \nWe begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs)\, exploring their architecture\, their use in image and video processing\, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing. \nIn the afternoon sessions we implement CNNs and RNNs using real data sets \nR Packages used: keras\, tensorflow \n \n \n \n \n \n Day 2\n DAY 2 \nAdvanced Supervised Learning Techniques \nOn day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture\, its self-attention mechanism\, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks\, and compare it to traditional machine learning methods. \nIn the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts. \nR Packages: keras\, tensorflow\, rstan\, brms\, BART \n \n \n \n \n \n Day 3\n DAY 3 \nUnsupervised Learning – Clustering and Dimension Reduction \nThe third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering\, DBSCAN\, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions. \nIn the afternoon session students will explore the use of these techniques through real-world data sets. \nR Packages: cluster\, dbscan\, factoextra\, Rtsne\, umap \n \n \n \n \n \n Day 4\n DAY 4 \nUnsupervised Learning – Anomaly Detection and Course Wrap-up \nOn the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection\, network security\, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning. \nIn the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor. \nR Packages: anomalize\, forecast\, e1071 \n \n \n \n \n \n \n \n \n Dr. Andrew Parnell\n \n Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science\, Nature Communications\, and Proceedings of the National Academy of Sciences\, and has methodological publications in journals such as Statistics and Computing\, Journal of Computational and Graphical Statistics\, The Annals of Applied Statistics\, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics\, time series modelling\, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change\, 3D printing\, and bioinformatics. \nResearch Gate\nGoogle Scholar\nORCID\nLinkedIn\nGitHub URL:https://prstats.preprodw.com/course/online-course-machine-learning-with-r-intermediate-advanced-mliapr/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2023/07/hunter-harritt-Ype9sdOPdYc-unsplash-scaled.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220518 DTEND;VALUE=DATE:20220521 DTSTAMP:20260418T225120 CREATED:20220218T165305Z LAST-MODIFIED:20220224T004857Z UID:10000350-1652832000-1653091199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Statistical Radiocarbon Dating And Age Depth Modelling (RDAD01) This course will be delivered live DESCRIPTION:Delivered remotely (Ireland)\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 – Western 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 This course will provide attendees with the basics to understand and implement age-depth models for partially dated stratigraphic data. The focus will be on radiocarbon dating but the approach extends to many other forms of dated information\, and will be relevant to those who have a wide variety of palaeo-environmental reconstruction problems. As is common in age-depth modelling\, the Bayesian paradigm will be used to create the age-depth models\, though no prior experience with Bayesian software or methods is required. The course will cover the use of multiple different R packages though the focus will be on the author’s own Bchron software. Attendees are encouraged to bring their own data sets and explore them using the tools covered during the course. \n \n \n \n \n \n Intended Audiences\n Research postgraduates\, practicing academics\, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data.\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time zone – Western European Time \nAvailability – 30 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n Teaching Format\n A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic knowledge of statistics and statistical models (e.g. generalised linear modelling) required. Basic experience of exploring data sets\, and fitting regression and generalised linear models with R required. \n \n \n \n \n \n Assumed computer background\n Some familiarity with R including the ability to import/export data\, manipulate data frames\, fit basic statistical models\, and generate simple exploratory and diagnostic plots.\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\nWednesday 18th – Classes from 09:30 – 17:30Day 1: Introduction to Radiocarbon dating; introduction to Bayesian statistics; basics of radiocarbon calibration \nThursday 19th – Classes from 09:30 – 17:30Day 2: Methods for calibrating radiocarbon dating; introducing prior information into radiocarbon date; basics of age-depth modelling \nFriday 20th – Classes from 09:30 – 17:30Day 3: Age-depth modelling approaches (Bacon\, Bchron\, Clam\, Oxcal); extending and using age-depth models in palaeo-environmental reconstruction \n\n  \n \n \n \n \n \n Course Instructor\n \n  \n  \nProf. Andrew Parnell \nWorks at – Teaches –  URL:https://prstats.preprodw.com/course/online-course-statistical-radiocarbon-dating-and-age-depth-modelling-rdad01-this-course-will-be-delivered-live/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/Screenshot-2022-01-12-at-17.28.45.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210908 DTEND;VALUE=DATE:20210911 DTSTAMP:20260418T225120 CREATED:20200828T195529Z LAST-MODIFIED:20220223T140559Z UID:10000316-1631059200-1631318399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Missing Data Analytics (MDAR01) DESCRIPTION:Delivered remotely (Ireland)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nSeptember\, January 8th\, 2021\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 – 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 Course Details\n This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package. \nThe course is structured over 3 days and includes classes on: \n\nAn introduction to missing data analysis terminology\, missing completely at random\, missing at random\, not missing at random\nA revision of likelihood and regression approaches\nThe Fully Conditional Specification (FCS) approach\nAn introduction to the mice package\nThe use of Bayesian and likelihood-based methods in missing data analysis\nBayesian missing data analysis using JAGS\nMore advanced missing data analysis including non-ignorable and not missing at random methods\n\n \n \n \n \n \n Intended Audiences\n Research postgraduates\, practicing academics\, or other professionals from any field who would like to learn about missing data analysis and how it can help them produce better quality information from their data. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Venue – Delivered remotely \nTime zone – Western European Time \nAvailability – 30 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of regression methods and generalised linear models. \n \n \n \n \n \n Assumed computer background\n Some familiarity with R including the ability to import/export data\, manipulate data frames\, fit basic statistical models\, and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \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 If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk \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 8th\n Classes from 09:30 to 17:30\nHow to run a missing data analysis in mice\nIntroduction to Bayesian analysis and missing data\nThe use of Bayesian and likelihood-based methods in missing data analysis\nThe fully conditional specification approach to missing data analysis\n \n \n \n \n \n Thursday 9th\n Classes from 09:30 to 17:30Including missing data in JAGS and StanAn introduction to the mice packageBayesian software tools JAGS/Stan for missing data analysis \n \n \n \n \n \n Friday 10th\n Classes from 09:30 to 17:30Advanced missing data analysis methodsMore advanced missing data analysis including non-ignorable and not missing at random methodsMissing data analysis in machine learning \n \n \n \n \n \n \n Course Instructor\n  \nProf. Andrew Parnell\nComing Soon URL:https://prstats.preprodw.com/course/online-course-missing-data-analytics-mdar01/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2020/08/mdar01.jpg GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR