Explore our Behavioural and Data Science taught Master's degree.

P-C803

MSc

1 year full-time

30 September 2024

Psychology

University of Warwick

Understand the underlying factors driving human behaviour on Behavioural and Data Science MSc. Warwick's Psychology department offers you training in basic psychology, behavioural economics and state of the art methods in data science and analytics.

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

You will have a combination of lectures, seminars and practical classes or workshops. Lectures introduce you to a particular topic, seminars build on that knowledge and workshops and practical classes allow you to put what you are learning into practice. Seminars, practical classes and workshops are smaller groups than lectures giving access to tutors to help you put into practice what you are learning.

Class sizes will naturally vary, however this course typically has around 25-30 students.

Teaching occurs throughout the week, with an average of 8-12 hours of lectures and 5-7 hours of practical classes or seminars per week. You will also have meeting with your personal tutor at intervals throughout your course.

We typically assess modules through a mix of assessment types, which include worksheets, essays, research reports, modelling and data analysis, class tests, exams, and presentations.

Your timetable

Your personalised timetable will be complete when you are registered for all modules, compulsory and optional, and you have been allocated to your lectures, seminars and other small group classes. Your compulsory modules will be registered for you and you will be able to choose your optional modules when you join us.

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.

• Band B
• IELTS overall score of 7.0, minimum component scores of two at 6.0/6.5 and the rest at 7.0 or above.

There are no additional entry requirements for this course.

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.

Psychological Models of Choice

The main aim of this module Psychological Models of Choice is to review theories of individual choice. Core empirical results in the decision-making literature will be reviewed and their theoretical implications explored.

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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