High-Dimensional Statistics with R (June 2024) About the Course This is a 2-day in-person workshop designed by the ED-DaSH team in the EPCC at the University of Edinburgh. Ed-DaSH is a Data Science training programme for Health and Biosciences. The team has developed workshops using The Carpentries platform and is now being used in the Data Upskilling Short Courses programme run by Edinburgh Carpentries. You can find out further information on Edinburgh Carpentries at The University of Edinburgh here. The model is based on a community of certified instructors who teach at the workshops, and contributors who maintain the lesson materials. The instructors come from a range of backgrounds with sound experience in dealing with data. Lesson materials are all available under a CC-BY licence and maintained by the community itself. They are used as the modular bricks to build a slightly different version of the workshop depending on the attendee needs. The target audience for this workshop are learners who already possess a solid understanding of statistics and linear models with R, and are interested in acquiring high-dimensional statistical techniques using R. All those who are in the scottish workforce or job-seekers in Scotland are eligible for fee waivers. We create a friendly environment for learning to empower you and enable data-driven good practices. The workshops are highly interactive and hands-on. Who is the course for? This short course is designed for individuals who already possess a solid understanding of statistics and linear models with R, and are interested in acquiring high-dimensional statistical techniques using R. All those who are in the scottish workforce or job-seekers in Scotland are eligible for fee waivers. Learning Outcomes The focus of the course is on biomedical and health sciences data, such as gene expression, DNA methylation, and health records, which often present challenges due to their extremely high-dimensional nature. Students will be introduced to various methods and approaches for disentangling patterns from natural variability in such datasets, with an emphasis on high-dimensional regression, dimensionality reduction, and clustering. By the end of the course, participants will be proficient in applying and critically analyzing a wide range of statistical methods. How will this course be delivered? This is a 2 day in-person course with a total of 12 hours of teaching. 7 hours each day inclusive of a 45-minute lunch break and several shorter tea breaks (10AM-5PM). Course fees and funding Course fees for 23/24 are £240 but funded places are available for people employed or unemployed in Scotland (residency requirements apply). Funding Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh. Eligibility Funded places are available to those who meet SFC fee waiver criteria: “Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.” If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria: be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and be ordinarily resident in Scotland at the course start date. You can find out more about residency criteria on the SAAS website or in this summary Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here. Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying. Please note that full-time students (including full-time PhD students) are not eligible for funding. What is received upon completion? You will receive a certificate of completion. How to apply Applications for June 2024 are now open. Your application will be processed in 1-2 weeks on a first come, first served basis with priority given to applicants who meet the criteria for a funded place. We aim to email all applicants within 2 weeks of submission regardless of the outcome of their application. APPLY HERE Applications will close on 4 June 2024. Jun 11 2024 00.00 - Jun 12 2024 23.59 High-Dimensional Statistics with R (June 2024) A short course aimed at familiarising learners with statistical and computational methods in R for the extremely high-dimensional data commonly found in biomedical and health sciences.
High-Dimensional Statistics with R (June 2024) About the Course This is a 2-day in-person workshop designed by the ED-DaSH team in the EPCC at the University of Edinburgh. Ed-DaSH is a Data Science training programme for Health and Biosciences. The team has developed workshops using The Carpentries platform and is now being used in the Data Upskilling Short Courses programme run by Edinburgh Carpentries. You can find out further information on Edinburgh Carpentries at The University of Edinburgh here. The model is based on a community of certified instructors who teach at the workshops, and contributors who maintain the lesson materials. The instructors come from a range of backgrounds with sound experience in dealing with data. Lesson materials are all available under a CC-BY licence and maintained by the community itself. They are used as the modular bricks to build a slightly different version of the workshop depending on the attendee needs. The target audience for this workshop are learners who already possess a solid understanding of statistics and linear models with R, and are interested in acquiring high-dimensional statistical techniques using R. All those who are in the scottish workforce or job-seekers in Scotland are eligible for fee waivers. We create a friendly environment for learning to empower you and enable data-driven good practices. The workshops are highly interactive and hands-on. Who is the course for? This short course is designed for individuals who already possess a solid understanding of statistics and linear models with R, and are interested in acquiring high-dimensional statistical techniques using R. All those who are in the scottish workforce or job-seekers in Scotland are eligible for fee waivers. Learning Outcomes The focus of the course is on biomedical and health sciences data, such as gene expression, DNA methylation, and health records, which often present challenges due to their extremely high-dimensional nature. Students will be introduced to various methods and approaches for disentangling patterns from natural variability in such datasets, with an emphasis on high-dimensional regression, dimensionality reduction, and clustering. By the end of the course, participants will be proficient in applying and critically analyzing a wide range of statistical methods. How will this course be delivered? This is a 2 day in-person course with a total of 12 hours of teaching. 7 hours each day inclusive of a 45-minute lunch break and several shorter tea breaks (10AM-5PM). Course fees and funding Course fees for 23/24 are £240 but funded places are available for people employed or unemployed in Scotland (residency requirements apply). Funding Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh. Eligibility Funded places are available to those who meet SFC fee waiver criteria: “Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.” If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria: be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and be ordinarily resident in Scotland at the course start date. You can find out more about residency criteria on the SAAS website or in this summary Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here. Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying. Please note that full-time students (including full-time PhD students) are not eligible for funding. What is received upon completion? You will receive a certificate of completion. How to apply Applications for June 2024 are now open. Your application will be processed in 1-2 weeks on a first come, first served basis with priority given to applicants who meet the criteria for a funded place. We aim to email all applicants within 2 weeks of submission regardless of the outcome of their application. APPLY HERE Applications will close on 4 June 2024. Jun 11 2024 00.00 - Jun 12 2024 23.59 High-Dimensional Statistics with R (June 2024) A short course aimed at familiarising learners with statistical and computational methods in R for the extremely high-dimensional data commonly found in biomedical and health sciences.
Jun 11 2024 00.00 - Jun 12 2024 23.59 High-Dimensional Statistics with R (June 2024) A short course aimed at familiarising learners with statistical and computational methods in R for the extremely high-dimensional data commonly found in biomedical and health sciences.
