The published course and module descriptions were accurate when first published and remain the basis of the course, but the University has had to modify some course and module content in response to government restrictions and social distancing requirements. In the event of changes made to the government advice and social distancing rules by national or local government, the University may need to make further alterations to the published course content. Detailed information on the changes will be sent to every student on confirmation in August to ensure you have all the information before you come to Oxford Brookes.
Data Analytics
MSc
Key facts
Start dates
September 2020 / September 2021
Location
Course length
Full time: 12 months
Part time: 2-5 years
Overview
With our MSc in Data Analytics you will learn fundamental theory and practice mathematical and statistical modelling. With special reference to data analysis and visualisation.
With recent developments in digital technology, society has entered the era of 'big data'. The UK Government recognises big data as one of the eight great technologies. It has priorities for funding and research and will have a pivotal role in rebuilding and strengthening the economy.
The explosion and wealth of available data in a wide range of application domains gives rise to new challenges and opportunities in all areas. One major challenge is how to take advantage of the unprecedented scale of data. And how to gain further insights and knowledge to improve the quality of offered products and services.
We designed the MSc in Data Analytics for those currently in employment. And to run alongside the MSc in Data Analytics for Government. It is available to all students, and is not exclusive to any particular employment sector.

How to apply
Entry requirements
Specific entry requirements
You should normally hold a good (first or second class) degree in the physical or social sciences which has developed analytical knowledge and understanding in mathematical sciences.
Typically this includes candidates with knowledge and familiarity with basic computing, mathematics and statistics concepts and methods at a degree level.
Applicants with other qualifications plus work experience from other fields who have quantitative skills and familiarity with data analysis and modelling ideas, to be reflected in their application, will also be considered. These applications must be approved by the Programme Lead.
Please also see the University's general entry requirements.
English language requirements
If your first language is not English you will require a minimum IELTS score of 6.0 overall with 6.0 in all components.
Please also see the University's standard English language requirements.
English requirements for visas
If you need a student visa to enter the UK you will need to meet the UK Visas and Immigration minimum language requirements as well as the University's requirements. Find out more about English language requirements.
Pathways courses for international and EU students
We offer a range of courses to help you meet the entry requirements for your postgraduate course and also familiarise you with university life in the UK.
Take a Pre-Master's course to develop your subject knowledge, study skills and academic language level in preparation for your master's course.
If you need to improve your English language, we offer pre-sessional English language courses to help you meet the English language requirements of your chosen master’s course.
Terms and Conditions of Enrolment
When you accept our offer, you agree to the Terms and Conditions of Enrolment. You should therefore read those conditions before accepting the offer.
Application process
Tuition fees
Questions about fees?
Contact Student Finance on:
Tuition fees
Fees quoted are for the first year only. If you are studying a course that lasts longer than one year your fees will increase each year.
Financial support and scholarships
Additional costs
Please be aware that some courses will involve some additional costs that are not covered by your fees. Specific additional costs for this course, if any, are detailed below.
Learning and assessment
The MSc in Data Analytics has a modular course-unit design. This provides you with 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)
- dissertation (60 credits).

Study modules
Please note: As our courses are reviewed regularly as part of our quality assurance framework, the modules you can choose from may vary from that shown here. The structure of the course may also mean some modules are not available to you.
Learning and teaching
Our course has a supportive teaching and learning strategy based on active student engagement.
We use a variety of teaching and assessment methods such as:
- critical appraisal reports
- data analysis reports
- data analysis using software applications
- presentations and case studies.
Learning methods include:
- blended learning
- formal lectures
- problem solving practicals
- guided independent learning
- use of the computer based virtual learning environment ‘Moodle’
- independent research
- software data analyses
- experiments.
Assessment
Assessment methods used on this course
We have designed the assessments on this course to develop your technical skills. This is led by the underlying theory and requirements of the industry.
Assessment is 100% coursework and covers a range of activities including:
- reports
- data analysis
- programming
- presentations.
We encourage you to relate the assessment tasks with professional activities. And to relate your achievements with professional standards.
You will have the opportunity to work independently and in groups. Where appropriate, we use self and peer assessment to encourage you to get involved in your own professional development.
Research
The School of Engineering, Computing and Mathematics is home to world-leading and award-winning research.
Our focus is on user-inspired original research with real-world applications. We have a wide range of activities from model-driven system design and empirical software engineering through to web technologies, cloud computing and big data, digital forensics and computer vision.
Staff and students collaborate on projects supported by the EPSRC, the EU, the DTI, and several major UK companies.
Computing achieved an excellent assessment of its UoA (Unit of Assessment) 11 return for REF 2014 (Research Excellence Framework).
Students on this course can be involved with research in the following research groups:
After you graduate
Career prospects
This programme allows graduates to undertake a wide range of roles in data science. Common careers in this area are as:
- data engineers
- business analysts
- data managers
- machine learning practitioners
- data scientists.
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.