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MSc Behavioural and Data Science

  • DeadlineStudy Details:

    MSc 1 year full-time

Masters Degree Description

This course offers training in the foundations of psychology, decision-making, behavioural economics and behaviour change. It will also develop your understanding of state-of-the-art methods in data science and data analytics, focusing on statistical methods, machine learning, and data visualisation.

You will gain an understanding of large-scale patterns in data, with an eye to comprehending the underlying factors driving human behaviour. This can be used to understand consumer behaviour, economics, politics, history, wellbeing, and many other large-scale patterns at national and international levels. Previous experience in behavioural science is not necessary, but you should have programming skills in at least one programming language (e.g., R, Python, Matlab, or others).

Skills from this degree

Graduates will be able to:

  • Use data to understand how and why people make the choices they do, and understanding the consequences of their choices in relation to public policy (e.g. encouraging people to save for pensions or change to low-carbon behaviours), industry (e.g. understanding how to place a new product in the market), and individual behaviour (e.g. understanding why people drink and eat too much)
  • Access and analyse large-scale datasets
  • Utilise state-of-the-art techniques in data analysis and visualisation
  • Design and conduct studies using data analysis to understand behaviour

Entry Requirements

Applicants are required, at a minimum, to have a degree in a relevant subject, e.g. Psychology, Computer Science, Mathematics, Economics, etc., equivalent to a UK 2:1 or greater in order to be considered. As we anticipate receiving a large number of applications, preference will be given to those with the strongest quantitative or social sciences backgrounds.

Evidence of experience with programming in Python or R is also preferred—at a minimum, students should have online or university instruction in programming in at least one programming language. The MSc in Behavioural and Data Science is a quantitative degree and students should feel comfortable taking a mathematical approach to their thinking before they join the course. The course requires students to undertake programming assignments and long-form essay assignments and so requires students to be comfortable in programming and to have very good written communication skills in English.

On the MSc, we cover the use of statistics and computational approaches to make sense of behavioural data (e.g., regression, t-tests, machine learning). We cover R, Python, and Matlab programming languages for statistics and mathematical modelling. We also use maths in psychological and computer science models.

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Fees

For fees and funding options, please visit website to find out more

Programme Funding

We offer a variety of postgraduate funding options for study at the University of Warwick, from postgraduate loans, university scholarships, fee awards, to academic department bursaries.

Student Destinations

Graduates from our MSc have followed career paths in postgraduate research and industry. Some have gone on to work on PhDs at the Alan Turing Institute and various Universities both in and outside the UK. Others have taken up positions as data scientists in industry, government, and NGOs.

Our department has a dedicated professionally qualified Senior Careers Consultant offering impartial advice and guidance together with workshops and events throughout the year.

Module Details

Core modules

Students will study seven core modules across Psychology and Computer Science, including a Behavioural and Data Science project. These modules include:

Integrated Behavioural and Data Science

This module covers thinking, writing, evaluating, project planning, and methodological integration of behaviour and data science.

Issues in Psychological Science

This module covers core psychology and behavioural science content relevant to later modules in the degree, including memory, attention, perception, personality and individual differences, choice, and subjective well-being. It will provide you with the psychological background to enable you to understand and critically evaluate material on those later modules. Through a combination of lectures, seminars, and laboratory-based sessions, you will learn about both models and data in the relevant areas of psychology. You will also learn basic MATLAB programming and model implementation.

Methods and Analysis in Behavioural Science

The purpose of the module is to introduce you to experimental design and statistical programming. Behavioural scientists need statistical analysis of experimental data and of large data sets. This module covers these topics to allow you to understand how to test hypotheses, plan experimental design and perform statistical analysis using R.

Foundations of Computing

The aim of the module is to equip you with a grounding in foundations of computing, to enable students from a wider background to confidently undertake a taught Master's programme in advanced computing topics.

Data Mining

This module will help you understand the value of data mining in solving real-world problems, as well as the foundational concepts underlying data mining. You will also understand the algorithms commonly used in data mining tools to gain the ability to apply data mining tools to real-world problems.

Optional modules

You will also choose two psychology-/behavioural science-focused optional modules, and two computer/data science-focused optional modules.

Optional modules can vary from year to year. Example optional modules may include:

Psychology-/behavioural science-focused optional modules:

  • Behavioural Change: Nudging and Persuasion
  • Neuroeconomics
  • Bayesian Approaches in Behavioural Science
  • Principles of Cognition
  • Behavioural Ethics

Computer/data science-focused optional modules:

  • Foundations of Data Analytics
  • Social Informatics
  • Natural Language Processing
  • Urban Data – Theory and Methodology
  • Interdisciplinary Approaches to Machine Learning
  • Data science across disciplines
  • Visualisation Foundations

The availability of option modules depends on several factors and cannot be guaranteed in advance. Therefore, the list above provides a sample of previously available options for illustrative purposes only.

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