Overview of course aims

This MSc course aims to upskill psychology graduates (and those from neighbouring disciplines) for a career in data science, with a particular emphasis on human-related datasets commonly encountered in industry and third sector organisations. Students will gain experience of working with diverse data types, using a wide range of analysis and visualisation techniques. They will also deepen understanding of key legislation and ethical issues surrounding the access and use of human behavioural data.

All core modules will be taught in R, a statistical programming language widely used for work of this nature. No prior coding experience is required: the course begins with the fundamental principles of working in R and builds on these skills throughout. The course culminates in a substantial piece of empirical work that applies an advanced statistical technique to a real-world question.

Overview of content

Core modules

Data Analysis & Programming in the Biosciences. This module introduces students to R and RStudio, assuming no prior coding experience. Students will become familiar with working in this statistical environment, running basic statistical tests, and documenting reproducible analyses.
Data Visualisation. Effective visualisation can simplify complex data and tell a compelling story, while poor visualisation can be unclear or even actively misleading. This module will walk students through the process of transforming raw complex data into clear meaningful visualisations.
Ethics and Data Governance. Regardless of the type of data being used, managing it securely and transparently is a key part of data science. This module covers essential aspects of data governance, including how to ensure quality, integrity, and security throughout the data life cycle.
Advanced Methods in Behavioural Research. This module introduces a range of modern analytical techniques used in both academic and commercial settings. Students will learn how each one can be used to tackle questions about complex real-world data, and evaluate their advantages and limitations.
Big Behavioural Data. When an individual uses social media, plays a video game, or makes an internet search, a digital trace of this interaction is created. This module develops students’ skills in understanding and utilising this large-scale data to generate insights for academic or policy use.

Optional modules

One optional module can be selected in Semester 2, allowing students to tailor the course to their individual interests or skill development. Examples include:

To further coding or statistical skills: Introduction to Programming (using Python), Data Analysis in Neuroimaging, or Advanced Regression Analysis.
To expand relevant psychological knowledge: Memory and the Brain, Human Risk, Microanalysis of Communication, or Psychology of Health.
To gain industry experience: the Advanced Work-Based Learning module offers the opportunity to complete an external placement.

Empirical project (MSc requirement)

Students will undertake a substantial piece of empirical work, applying their advanced data and statistical skills to a real-world question using human data. Projects typically use secondary datasets, such as those available through national cohort studies or smart data donation, and are supervised by experts experienced with these types of data. The final project report includes a literature review that situates the research question in context and motivates the analytical approach, a comprehensive report of the methods and results, and interpretation and discussion of the findings.

Literature review (Diploma only)

The opportunity to work towards a Diploma qualification is offered to students whose work does not reach the standard of a MSc qualification. For these students, a literature review is required in place of an empirical project. The literature review aims to develop Diploma students' ability to critically appraise different methodological approaches in addressing a specific question about human behaviour. Through this assignment, students will demonstrate an ability to think and write about analytical approaches in their chosen field of study in a coherent and insightful way. The literature review takes place during the summer period. The 6000 word final essay accounts for 100% of the module mark.