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This unit will provide students with the skills needed to engage in reproducible data science. Students will learn how to wrangle data, build data visualisations, and model their data using the open source data science software, R. Each of the sessions will be run as a combined seminar and hands-on coding workshop. Students will learn how to use a reproducible workflow to generate reproducible analysis. They will also learn about general computational skills such as using git and GitHub for version control, and Binder for building reproducible computational environments.  Graduates with data science skills are in high demand, with skills in using R particularly desirable to employers across the academic, industrial, and business sectors. This unit will provide students with a grounding in data science using R and the knowledge to build on this foundation for the development of more focused skills (such as machine learning using R).

The unit aims to increase the students understanding of the following:

•      To familiarise students with the tools to engage in reproducible research and data science practices.

•      To familiarise students with the principles of Reproducibility and Open Science (incl. pre-registration of experiments, open data, and open analysis) and the problems that arise from Questionable Research Practices (QRPs).

•      To familiarise students with the principles of programming and analysis in R (incl. linear mixed models), and the use of R Markdown or generate reproducible analyses and presentations.

•      To provide students with the experience of advanced decision-making in the application of different statistical tests to different research questions.

•      To provide students working in small groups with the experience of using and programming in R for reproducible data analysis.

This module will consist of 6 workshops – each workshop will involve a mix of seminar and hands-on programming.  The six workshops are as follows:

1. Reproducibility and R
2. The Linear Model (Regression)
3. The Linear Model (ANOVA)
4. Mixed Models
5. Data Simulation and Advanced Data Visualisation
6. Reproducible Computational Environments and Presentations
 

Practical sessions in computer labs.

Demonstrate an understanding of the principles of Open Science and the need for reproducibility in research.
Develop an understanding of the logic underlying the use of programming and building statistical models in R, and the range of circumstances appropriate for their use.

Design and interpret complex statistical models using diverse approaches.

Acquire experience of cutting edge data science methodologies for reproducible research.

Problem solving.
Programming.
Data presentation.
Time management.

One R-based assignment produced using R Markdown worth 100%.

During the hands-on coding sessions, students will receive formative feedback associated with each of the practical problems that they will be engaged with.

Grolemund, G, & Wickham, H. (2017). R for Data Science, O’Reilly. (https://r4ds.had.co.nz)

The year studying abroad is an integral part of the course and must be completed. Some costs, such as tuition fees, are substantially lower. Other costs can vary and are not included in the tuition fee. For example, living costs may be higher or lower than the UK, and there are travel-related costs to consider, such as paying for a passport, visa fees, health insurance, and (if needed) vaccinations.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's (PDF document, 91KB).

Students participating in placements outside the UK may be able to apply for funding from the depending on eligibility. Priority will be given to students from low income households.