Theme: Mathematics & Statistics

An Introduction to R: Software For Statistical Analysis, with Dr Simon R. White, MRC Biostatistics Unit, and Dr Adam P. Wagner, University of Cambridge.

GNU R is (freely) available for all major platforms (Microsoft Windows, Linux, Mac, etc.) and is growing in popularity in academia and beyond for carrying out statistical analysis and data manipulation.

The aim of the course is to introduce participants to the basics of statistical analysis and the open source statistical software GNU R.

Participants will actively use R throughout the course, during which they will be introduced to principles of statistical thinking and interpretation by example, exercises and discussion about a range of problems. The examples will be used to present a variety of statistical concepts and techniques, with no focus on any specific discipline.

Participants Without a Raven Password: If you do not have a Raven's account and would like to attend this course, or have other booking queries, please email Adam Wagner (apw40@medschl.cam.ac.uk).

PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions.

This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

« Description not available »

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we introduce the R language, and cover basic data manipulation and plotting. We explore more advanced data analysis techniques using the packages dplyr and ggplot. Finally we introduce the concept of reproducible research, and how this may be assisted using 'literate programming'—combining documentation with code.

After the course you should feel confident to start exploring your own dataset, using the materials and references provided.

Sessions

If you book onto this course you must attend all of the sessions as detailed below. Failure to attend a session or cancellation of your place less than 48 hours before the start of the first session will result in an administrative charge of £50.

Please ensure you have permission from your supervisor to attend this course before you make your booking!

Trainers

Dr Michael Grayling, MRC Biostatistics Unit

Dr Simon Frost, Department of Veterinary Medicine

Dr Matt Castle, GSLS

PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions.

This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.