Behavioural Data Science MSc
About this course
In virtually all sectors of the modern economy, understanding behaviour is critical. From healthcare and education to the services sector, businesses and organisations recognise the value of harnessing vast quantities of data to provide insights into how people behave. Combined with an understanding of psychological theory, this can generate insights that help explain complex patterns of human behaviour that lead to informed decision making that can drive progress and innovation.
Our innovative MSc Behavioural Data Science course focuses on bringing together state-of-the-art data science techniques with advanced psychological theory. You will leave with a unique ability to extract meaningful, theory-based insights from complex human data, as well as a diverse portfolio of work to demonstrate these skills to future employers.
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Stand out from your peers
Whether you want to get into data science roles that explore human behaviour, or already work in a related role, this course can help you differentiate yourself.
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Get the context
This course covers both the data science, and the psychology; from programming, machine learning, and data modelling, to understanding behaviour, cognition, and neuroscience.
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Set days in university each week
With 3 set days in university each week (2 for part-time students), balance your studies around your other commitments such as part-time work.
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Benefit from excellent support
from our academic team to enhance your learning.
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(out of 1,049 evaluated institutions)
What you’ll study
This course covers a broad range of topics and skills related to social and behavioural data and its analysis, both from an academic scientific point of view and from the point of view of a professional data scientist. Many of these topics and skills—machine learning and artificial intelligence; proficiency with a range of quantitative tools and programming languages; big data analysis; data visualisation; management of complex, unstructured data sets; reproducible data analysis; and others—are currently in high demand in both academic scientific research exploring human behaviour and in data science roles that explore behavioural data.
For those wishing to explore a more general data science course that does not focus purely on behavioural data, please see the MSc Data Science course.
Computational Statistics for Behavioural Data Science
This module aims to introduce you to advanced statistical modelling techniques used in data science, in both academic and non-academic contexts. You will be provided an advanced theoretical introduction to major types of statistical models, to statistical inference, and to statistical model evaluation and comparison. You'll also gain practical experience of state-of-the-art computational tools for performing these techniques.
Psychology of Cyberspace and Online Behaviours
This module will introduce students to the psychological aspects of cyberspaces - both in the form of the Internet and of other digital technology environments - and models of behaviours mediated by ICTs and other online technologies. The aim is to develop a critical awareness of the uses of contemporary technology, viewed from the perspective of psychology and neighbouring disciplines (such as Human-Computer-Interaction, Communication Studies, Criminology), and to provide a theoretical psychological background to understand how and why people behave the way they do in these spaces.
Python for Behavioural Data Science and Statistics
This module provides a general introduction to programming with Python, as well as a comprehensive introduction to using Python for data science and machine learning. You'll gain both theoretical understanding and practical experience of advanced techniques such as supervised and unsupervised machine learning, and deep learning using artificial neural networks.
Visualisation of Behavioural Data and Data Dashboards
This module provides a comprehensive introduction to data visualisation and interactive data dashboards using state-of-the-art and industry-standard tools. You are introduced to the theoretical principles of effective data visualisation for behavioural data, and gain extensive practical experience visualising a range of data types using a variety of plotting techniques. The module also provides a comprehensive introduction to producing publication-quality visualisations for scientific and other research reports, and covers how to produce these in a dynamic and reproducible manner.
Data Analysis for Cognitive Neuroscience
This module will introduce students to a variety of methods in cognitive neuroscience, with a focus on the analysis of physiological data to provide insights into human behaviour and cognition. You'll be introduced to a range of approaches in cognitive neuroscience, and consider the unique challenges associated with analysing the data these approaches produce.
Testing Psychological Theories Using Structural Equation Modelling
The aims of this module are to introduce you to the theoretical and philosophical underpinnings of structural equation modelling (SEM) and to equip you with the skills, and understanding, to appropriately construct, analyse, and interpret theoretical path analytic Confirmatory Factor Analysis (CFA), and SEM models.
Professional Skills for Behavioural Data Science
This module will prepare you to apply behavioural data science knowledge and skills in a professional or research context. Through a series of interactive lectures/workshops, you will develop key employability skills including self-directed learning, communication, and project management.
Behavioural Data Science Research Project
This module will enable students to develop and demonstrate the skills necessary to plan, conduct, and report on an independent behavioural data science research project. You'll use existing literature to guide the development of one or more research questions, and select a suitable data source and appropriate software and tools to analyse the data and address the research question.
We regularly review and update our course content based on student and employer feedback, ensuring that all of our courses remain current and relevant. This may result in changes to module content or module availability in future years.
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How you're taught
This course starts in September. The course is completed in one year of full-time study (your final piece of coursework is submitted in late August), or two years part-time. The teaching terms run from late September to Christmas and then from January to Easter. Part-time students attend for two of the three days only.
Study and support
The course will be delivered through a combination of lectures, workshops, seminars, and independent study. Your learning will be supported by high-quality, interactive online materials via the University’s Virtual Learning Environment (VLE). Throughout the course you will develop a portfolio of work that can be used to showcase your behavioural data science skills to prospective employers.
The blend of data science and psychology is reflected in the course team: you will be taught by academics with a wide range of experience working with human behavioural data, as well as specific expertise across multiple areas of psychology and who have run data science courses for national and international organisations.
Alongside formal lessons, all students receive personal tutoring using a small group tutorial system. A tutorial group will be held on a bi-weekly basis, where you'll be given the opportunity to reflect on practice and experiences on the course, and to provide a place for interaction and exchange with your fellow students. You will also be assigned a research project supervisor, who can provide further support and development.
In-sessional English language support
In-sessional English language support classes are available to all international (non-EU) students studying on degree courses at NTU. There is no extra charge for these classes.
How you're assessed
The main assessment methods to be used on the MSc Behavioural Data Science are:
- Reports
- Essays
- Portfolios
- Presentations
- Exams
- Full research project
The type and timing of assessments has been examined holistically across the course to ensure diversity and evenness of workload.
Formative feedback occurs throughout the course as students complete, and receive immediate feedback on, tasks in interactive workshops. Where theoretical content is heavier, regular online quizzes are provided to enable students to check their understanding.
Careers and employability
Your future career in behavioural data science
Data science as a discipline is now found in many large organisations across private businesses, the health and medical sectors, in government, and in the IT (especially web-based) services sector, and as such represents one of the key jobs of the future.
It is a growing field as society produces not only more data about how people behave, but also data that are more widely and easily accessible, alongside increasingly advanced tools for gaining insights. In this context it is important that data scientists working on human data are able to not simply gain insights about trends in data, but use psychological knowledge to understand these.
If you are already qualified or work in the fields of computer science or engineering, this Masters course can help you stand out from your peers. If you're already working in data science, this course is ideal to continue your professional development by expanding your behavioural and statistical expertise.
Outside of industry, data science skills in academia are also increasingly in demand, and this MSc would also provide the skills required to move into highly skilled research roles or commence a PhD.
Employability team
Our expert Employability team will work closely with you at every stage of your career planning, providing personal support and advice. You can benefit from this service at any time during your studies, and for up to three years after completing your course. Find out more about the service.
Campus and facilities
As a postgraduate Psychology student, you’ll mainly be studying in the Chaucer, Taylor and Newton buildings, at the centre of our vibrant City Campus. As well as a range of classrooms and lecture theatres, you’ll benefit from our dedicated Psychology learning environments.
We have specialist research laboratories including eye-trackers, motion capture labs, virtual reality and driving simulation suites, a mock prison cell, and an £80,000 EEG system. These facilities support your learning alongside staff research in the exciting areas of human cognition, behavioural neuroscience, human interaction and communication, and human development.
NTU’s City Campus has everything you’ll need to stay busy between lectures. As well as the Boots Library and its beautiful roof garden, there’s our stylish Students’ Union building and two-storey, 100-station gym; a whole host of cafés, bars, restaurants and food outlets for every taste; our much-loved Global Lounge; performance and rehearsal spaces for musicians; and much, much more!
Take a few steps off campus and you’ll find yourself in the heart of Nottingham — one of Britain’s top 10 student cities, and one of Europe’s top 25. It’s stuffed with history, culture, and well-kept secrets to discover at your leisure. Enjoy lush green spaces, galleries, hidden cinemas and vintage shopping by day, and an acclaimed food, drink and social scene by night.
Take our virtual tour to get a real feel for the campus.
Entry requirements
UK students
Applying with prior qualifications
You'll need an undergraduate degree (minimum 2.2) in Psychology (or a related subject) or other recognised equivalent qualification. Related subject areas include subjects from social science, science, humanities and business.
Applying with non-standard entry qualifications/experience
Applicants without such qualifications will be considered on an individual basis but will be required to demonstrate how their experiences and knowledge would enable them to study this course at Masters level in their personal statement.
Additional requirements for UK students
Your application form requires a written statement in which you should outline reasons for wishing to undertake the MSc Behavioural Data Science. We will be looking to ensure that you have a sound rationale for joining the course based on:
- a realistic appreciation of the practicalities of modern data science, and
- an understanding of the specific focus of this course (i.e., the application of psychological theory to complex quantitative data).
Importantly, we will be checking for evidence of interest in, and aptitude for, quantitative data analysis and/or computer programming. Appropriate theoretical knowledge or experience working with complex quantitative data (human or otherwise) will be considered advantageous.
Other qualifications and experience
We welcome applications from students with non-standard qualifications and learning backgrounds and work experience. We consider credit transfer, vocational and professional qualifications, and any work or life experience you may have.
You can view our Recognition of Prior Learning and Credit Transfer Policy which outlines the process and options available, such as recognising experiential learning and credit transfer.
Getting in touch
If you need more help or information, get in touch through our enquiry form.
International students
Academic entry requirements: You'll need an undergraduate degree (minimum 2.2) in Psychology (or a related subject) or other recognised equivalent qualification. Related subject areas include subjects from social science, science, humanities and business. We accept equivalent qualifications from all over the world. Please check your international entry requirements by country.
Applicants with non-standard entry qualifications and/or relevant experience will be considered on an individual basis. You will be required to demonstrate how your experiences and knowledge would enable you to study this course at Masters-level in your Personal Statement.
English language requirements: See our English language requirements page for requirements for your subject and information on alternative tests and Pre-sessional English.
Additional requirements for international students
Your application form requires a written statement in which you should outline reasons for wishing to undertake the MSc Behavioural Data Science. We will be looking to ensure that you have a sound rationale for joining the course based on:
- a realistic appreciation of the practicalities of modern data science, and
- an understanding of the specific focus of this course (i.e., the application of psychological theory to complex quantitative data).
Importantly, we will be checking for evidence of interest in, and aptitude for, quantitative data analysis and/or computer programming. Appropriate theoretical knowledge or experience working with complex quantitative data (human or otherwise) will be considered advantageous.
English language requirements
View our English language requirements for all courses, including alternative English language tests and country qualifications accepted by the University.
If you need help achieving the language requirements, we offer a Pre-Sessional English for Academic Purposes course on our City campus which is an intensive preparation course for academic study at NTU.
Other qualifications and experience
We welcome applications from students with non-standard qualifications and learning backgrounds and work experience. We consider credit transfer, vocational and professional qualifications, and any work or life experience you may have.
You can view our Recognition of Prior Learning and Credit Transfer Policy which outlines the process and options available, such as recognising experiential learning and credit transfer.
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Getting in touch
If you need advice about studying at NTU as an international student or how to apply, our international webpages are a great place to start. If you have any questions about your study options, your international qualifications, experience, grades or other results, please get in touch through our enquiry form. Our international teams are highly experienced in answering queries from students all over the world.
Policies
We strive to make our admissions procedures as fair and clear as possible. To find out more about how we make offers, visit our admissions policies page.
