High-Dimensional Statistics with R workshop, Jan 17 - 20 Overview This course is intended for those who have a working knowledge of statistics and linear models with R and wish to learn high-dimensional statistical methods with R. This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records). These datasets can be challenging to approach, as they often contain many more features than observations, and it can be difficult to distinguish meaningful patterns from natural underlying variability. To this end, we will introduce and explain a range of methods and approaches to disentangle these patterns from natural variability. After completion of this course, learners will be able to understand, apply, and critically analyse a broad range of statistical methods. In particular, we focus on providing a strong grounding in high-dimensional regression, dimensionality reduction, and clustering. General Information Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting. When: 17 - 20 January 2023. Requirements: Participants must have access to a computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). Accessibility: We are dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you. Schedule The lesson taught in this workshop is being piloted and a precise schedule is yet to be established. Day 1 : 17th January (09:30 -13:00) Pre-workshop Setup Lessons High-Dimensional Statistics with R Introduction Regression with many features Break Morning 10:45 - 11:00 Day 2 : 18th January (09:30 -13:00) Lessons High-Dimensional Statistics with R Regularised regression Break Morning 10:45 - 11:00 Day 3 : 19th January (09:30 -13:00) Lessons High-Dimensional Statistics with R Principal component analyses Factor analysis Break Morning 10:45 - 11:00 Day 4 : 20th January (09:30 -13:00) Lessons High-Dimensional Statistics with R K-means clustering Hierarchical clustering Break Morning 10:45 - 11:00 Related Links Ed-DaSH home Jan 17 2023 09.30 - Jan 20 2023 13.00 High-Dimensional Statistics with R workshop, Jan 17 - 20 This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records). Register here Register here
High-Dimensional Statistics with R workshop, Jan 17 - 20 Overview This course is intended for those who have a working knowledge of statistics and linear models with R and wish to learn high-dimensional statistical methods with R. This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records). These datasets can be challenging to approach, as they often contain many more features than observations, and it can be difficult to distinguish meaningful patterns from natural underlying variability. To this end, we will introduce and explain a range of methods and approaches to disentangle these patterns from natural variability. After completion of this course, learners will be able to understand, apply, and critically analyse a broad range of statistical methods. In particular, we focus on providing a strong grounding in high-dimensional regression, dimensionality reduction, and clustering. General Information Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting. When: 17 - 20 January 2023. Requirements: Participants must have access to a computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). Accessibility: We are dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you. Schedule The lesson taught in this workshop is being piloted and a precise schedule is yet to be established. Day 1 : 17th January (09:30 -13:00) Pre-workshop Setup Lessons High-Dimensional Statistics with R Introduction Regression with many features Break Morning 10:45 - 11:00 Day 2 : 18th January (09:30 -13:00) Lessons High-Dimensional Statistics with R Regularised regression Break Morning 10:45 - 11:00 Day 3 : 19th January (09:30 -13:00) Lessons High-Dimensional Statistics with R Principal component analyses Factor analysis Break Morning 10:45 - 11:00 Day 4 : 20th January (09:30 -13:00) Lessons High-Dimensional Statistics with R K-means clustering Hierarchical clustering Break Morning 10:45 - 11:00 Related Links Ed-DaSH home Jan 17 2023 09.30 - Jan 20 2023 13.00 High-Dimensional Statistics with R workshop, Jan 17 - 20 This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records). Register here Register here
Jan 17 2023 09.30 - Jan 20 2023 13.00 High-Dimensional Statistics with R workshop, Jan 17 - 20 This is a short course aimed at familiarising learners with statistical and computational methods for the extremely high-dimensional data commonly found in biomedical and health sciences (e.g., gene expression, DNA methylation, health records).
