The MSc in Data Analytics has a modular course-unit design providing you with maximum flexibility and choice. To qualify for a master’s degree, you must pass modules amounting to 180 credits. This comprises twelve compulsory taught modules (10 credits each) plus your dissertation (60 credits).
Semester 1
Data Science Foundations – this module presents an overview of core data science concepts and tools, focusing on real-life data science research questions with practical exposure to R and/ or Python programming as an integral part of the course.
Statistical Programming – this module introduces core programming techniques in R essential for performing data manipulation, data processing and data analyses of traditional and alternative data sources through practical sessions.
Introduction to Machine Learning - This module provides the students with the principles of computer learning and its applications. It covers the fundamentals of machine learning methodologies, implementations and analysis methods appropriate for machine learning applications.
Introduction to Survey Research – this module introduces the stages involved with planning and undertaking surveys. The module will consider the methodological issues that may arise, including errors, and will discuss options for minimising the impact through the survey design.
Regression Modelling - This module will introduce the basic regression model - residual analysis, model building and selection, and the handling of categorical variables. Also, Logistic regression (binary response regression) will be introduced, assessing the model fit and model building and selection. Finally, Multiple regression and Multivariate regression modelling will be introduced.
Time Series Analysis - This module introduces students to time series and forecasting methods.
Semester 1 and 2
Research and Study Methods – This module will equip students with the skills necessary to perform research and employ effective study methods which will underpin their dissertations.
Semester 2
Survey Fundamentals – this module provides an overview of sampling and estimation fundamentals.
Advanced Machine Learning - This module builds on the Intro to Machine Learning module. It focuses on Advanced Programming Skills and Neural Computing as an extension of machine learning, natural language processing & multi-media. It considers supervised and unsupervised machine learning algorithms (random forests, neural networks, clustering, Log regression, and support vector machines) alongside more advanced Imaging and multi-media data processing.
Advanced Statistical Modelling - This module introduces a broad class of linear and nonlinear statistical models and the principles of likelihood inference to a variety of commonly encountered data analysis problems in variety of disciplines.
Introduction to Distributed Systems - This module provides an overview of processing data at large scale and parallel processing. It introduces Hadoop and Spark and the use of parallel processing paradigms.
Data Visualisation - This module builds on the basic data visualisations introduced in the compulsory modules. It will cover information design, interaction design and user engagement; state of the art tools to build useful visualisations for different types of data sets and application scenarios.
Students on the MSc are also required to pass complete a dissertation on a data science focussed topic related to their programme of study. The exact content of each dissertation will vary in accordance to the title but will involve the student completing a literature review and research of the topic at an advanced level, the preparation of a project proposal, the application of analytical techniques and academic approaches to the generation of alternative solutions and synthesis of a solution for the complex problem in hand, together with the presentation of the solution in oral and written form.
As courses are reviewed regularly as part of our quality assurance framework, the module lists you choose from may vary from the ones shown here.
Teaching and learning
The programme follows a supportive teaching and learning strategy based on active student engagement.
Students experience a variety of teaching and assessment methods. Some modules feature critical appraisal reports, data analysis reports, data analysis using software applications, presentations and case studies. Learning methods include blended learning, formal lectures and problem solving practicals, but also guided independent learning, use of the computer based learning environment ‘Moodle’, independent research, software data analyses, experiments and the like.
Approach to assessment
By paying due regard to the Oxford Brookes University’s Assessment Compact, the assessments on this programme have been designed to develop the learning of technical skills, shaped by the underlying theory and requirements of the industry. Assessment does not present students with a set of hurdles, but rather guides them through the staged acquisition of a complex set of professional skills, so that, by the time they graduate, they are ready to play an effective role in their chosen career. Feedback on the assessment tasks will be provided in a timely manner, emphasising the achievement of the learning outcomes of the modules and the programme.
Assessment on this programme is 100% coursework and will cover a range of activities including reports, data analysis, programming, and presentations – the exact range depending on the modules chosen. You will have the opportunity to work in groups and individually. Students will be encouraged to relate the assessment tasks, with professional activities, and to relate their achievements with professional standards. Where appropriate, self and peer assessment will be used to encourage students to involve themselves in their own professional development.
Please visit our Assessment Compact pages for more details.
Additional costs
The most likely extra costs involved in studying this course are computers, books and printing.
You are not required to have your own computer though many students choose to. We have computer rooms available 24 hours a day and 7 days a week on campus, so you should always be able to work if you don't have your own machine. Students who choose to buy a computer are advised to avoid low-end machines since reliability will be important; a budget of around £700 should suffice, although some modules may require use of higher-end hardware which is available on campus. The School does not provide any Apple MacOS machines and their use is not required, but their use is supported if you wish to bring your own. Linux machines are provided on campus and we support the use of Debian/Ubuntu distributions. If you wish for more detailed advice on machine specifications, the department will be happy to advise.
Most software used on the course is freely available to students, either because it is open source, or because the School has a licence which supports personal student use. The School is a member of the Microsoft Imagine scheme and other programs designed to grant access to professional-grade software tools, which would normally be very expensive, for free.
Your University library membership includes access to an extensive electronic library so in many cases, you will be able to read coursebooks online without extra cost. You can of course buy physical copies of books as well if you wish. Textbooks generally vary in price between £20 and £60 depending on the degree of specialism.
Most submission of work for the modules is electronic, so there is little need for printing.
Attendance pattern
Part time study is an option on this programme for students who wish to combine their study with work. Where possible we try to ensure that part time students only need to attend for 1 day a week, although students will be expected to undertake additional independent study.
Programme changes
On rare occasions we may need to make changes to our course programmes after they have been published
on the website. For more information, please visit our
Changes to programmes
page.