Introduction to Coding in R
Key information
- Start date:
- November 2024
- Attendance dates:
- To be confirmed
- Time commitment:
- Five days face-to-face teaching, followed by five days of independent project development and assessment (remote)
- Venue:
- Kate Granger Building, 30 Priestley Road, Surrey Research Park, Guildford, Surrey GU2 7YH
- Contact details:
- Matt Spick
- Email: matt.spick@surrey.ac.uk
- Start date:
- July 2025
- Attendance dates:
- To be confirmed
- Time commitment:
- Five days face-to-face teaching, followed by five days of independent project development and assessment (remote)
- Venue:
- Kate Granger Building, 30 Priestley Road, Surrey Research Park, Guildford, Surrey GU2 7YH
- Contact details:
- Matt Spick
- Email: matt.spick@surrey.ac.uk
Overview
In all aspects of modern quantitative research, there is a need for competency in the application of computational data analysis techniques. The R language and environment provides the means to manipulate, integrate, and interrogate data. R includes a wide variety of statistical, machine learning, as well as data visualisation packages, and is one of the most widely used programming languages for the sciences. This course is specifically designed for and targeted at those with no prior computational background or experience, who wish to enhance their data analysis skillset.
Learning will draw on real-world examples, with an emphasis on those from biological, health, medical, and social sciences. The course will provide coding expertise in a computer laboratory environment delivered by in-person teaching, feedback, and positive reinforcement of good coding practices.
Learning outcomes
The key learning outcomes from this course are:
- An understanding of data science workflows.
- The ability to install, run, and use a coding environment, including the handling of libraries and datasets.
- Confidence in cleaning and preparing data for analysis.
- An understanding of how to perform statistical tests efficiently in a coding environment.
- An ability to use both supervised and unsupervised data science methods.
- The ability to prepare journal or thesis-ready data visualisations, plots, and graphs.
Course content
Topics to be covered include:
- Introduction to the R environment and R Studio
- Data science workflows
- Principles of data handling
- Statistical tests and packages
- Effective data visualisation
- Data exploration and dimensionality reduction
- Machine learning with R.
Learning and teaching methods
The course will run over two weeks. The first week will be held in-person, in a computer lab on the Manor Park, University of Surrey campus. The structure for this first week will follow a format of an initial face-to-face lecture for each topic to introduce key concepts and examples, followed by interactive workshops to apply these concepts using the R coding language. Subsequently, students will spend one week completing a code-based project and submit this for assessment. Students may choose to carry out the project on their own data, or they may select from a number of available datasets and topics. Additional learning materials will be provided ahead of the course, as well as following the face-to-face week, for independent study.
Course leaders
Course leads
![Matt Spick profile image](/sites/default/files/styles/image_150x150_scale_and_crop/public/2024-08/Matt-Spick_jpeg.jpg?itok=zUpWdcV3)
Matt Spick
Lecturer in Health and Biomedical Data Analytics
![Nophar Geifman profile image](/sites/default/files/styles/image_150x150_scale_and_crop/public/2022-06/NG_s%20copy.jpg?itok=_awP_SOM)
Professor Nophar Geifman
Professor of Health and Biomedical Informatics
Entry requirements
The course is targeted at post-graduate research students and early career researchers in quantitative subjects, but will also be open to those from a qualitative background. No prior coding or computational background is required.
Fees and funding
Price per person:
£625
Standard fee£575
Fee for University of Surrey students and staffHow to apply
Please contact Matt Spick (matt.spick@surrey.ac.uk) to register your interest.
Terms and conditions
When you accept an offer of a place at the University of Surrey, you are agreeing to comply with our policies and regulations and our terms and conditions. You are also confirming you have read and understood the University's prospective student privacy notice.
Further details of our terms and conditions will follow.
Disclaimer
This online prospectus has been prepared and published in advance of the commencement of the course. The University of Surrey has used its reasonable efforts to ensure that the information is accurate at the time of publishing, but changes (for example to course content or additional costs) may occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for a course with us. Read the full disclaimer.
Course location and contact details
Campus location
Stag HillStag Hill is the University's main campus and where the majority of our courses are taught.
- Email: matt.spick@surrey.ac.uk
University of Surrey
Guildford
Surrey GU2 7XH