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Data Analytics for Government
September 2023 / September 2024
Part time: 2-5 years
Combine computing, statistical and mathematical skills and deal with the challenges of our modern data-driven society with our MSc Data Analytics for Government.
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.
We have structured this course in conjunction with the Office for National Statistics. Specifically for employees of all public sector bodies in the UK. You will develop skills in state-of-the-art methods and techniques related to data analytics. And you will cover cutting-edge areas in data analytics, including:
- data science and big data models
- advanced machine learning (with AI)
- statistical programming
- distributed systems (cloud, Hadoop, Spark)
- data visualisation
- statistics in government
- time series.
How to apply
Specific entry requirements
This programme is restricted to employees of all public sector bodies in the UK. Please contact the University if you are unsure if this applies to you.
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.
An equivalent English language qualification acceptable to the University.
Please also see the University's standard English language requirements.
International qualifications and equivalences
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.
Questions about fees?
Contact Student Finance on:
Questions about fees?
Contact Student Finance on:
+44 (0)1865 483088
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.
Please be aware that some courses will involve some additional costs that are not covered by your fees. Specific additional costs for this course are detailed below.
|Additional costs||Amount (£)|
|Travel and associated costs if relevant when undertaking work placements.||£30-700 per year|
If you are considering bringing your own computer, most of the software we use is on Windows machines though there is some use of Linux. We do not use Apple MacOS and their use is not required but some students do choose to bring MacOS machines so a Mac can be a viable choice if you so wish.
It’s your responsibility to cover print / binding costs where coursework submission is required. Please note that a lot of the coursework is now submitted online.
|You may choose to purchase books to support your studies. Many books on our reading lists are available via the Library, or can be purchased secondhand.||£20-60 per book|
Accommodation fees in Brookes Letting (most do not include bills)
|£94-265 per week|
Accommodation fees in university halls (bills included, excluding laundry costs)
|£122-180 per week|
Graduation costs include tickets, gowning and photography. Gowns are not compulsory but typically students do hire robes, starting at £41.
Students are responsible for their own travel to and from university for classes. BrookesBus travel is subsidised for full-time undergraduate students that are on a course with a fee of £9,250 or more, or living in an Oxford Brookes hall of residence. There is an administration fee for the production of a BrookesKey.
Funding your studies
Financial support and scholarships
Featured funding opportunities available for this course.
All financial support and scholarships
Learning and assessment
The MSc in Data Analytics for Government 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)
- your dissertation (60 credits).
You can study full time and complete the course in a year. Alternatively you can study part time and complete the course in 2, 3, 4 or 5 years.
Statistics in Government (10 credits)
This module provides a sound overview of the issues and challenges for Official Statistics in the UK.
Data Science Foundations (10 credits)
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.
Survey Fundamentals (10 credits)
This module provides an overview of sampling and estimation fundamentals.
Statistical Programming (10 credits)
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 Survey Research (10 credits)
This module introduces the stages involved with planning and undertaking surveys. It will consider the methodological issues that may arise, including errors, and will discuss options for minimising the impact through the survey design.
Regression Modelling (10 credits)
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.
Advanced Statistical Modelling (10 credits)
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.
Time Series Analysis (10 credits)
Analysis of univariate time series: description, modelling and forecasting. This module is aimed at the students who wish to gain a working knowledge of time series and forecasting methods.
An Introduction to Machine Learning (10 credits)
This module provides you 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.
Advanced Machine Learning (10 credits)
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.
Introduction to Distributed Systems (10 credits)
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 (10 credits)
This module will build 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 variations for different types of data sets and application scenarios; mapping.
Survey Data Collection (10 credits)
Further Survey Estimation Methods (10 credits)
Applied Data Mining (10 credits)
Dissertation in Data Analytics (60 credits)
Students on the MSc are also required to 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 you 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.
Please note: As our courses are reviewed regularly as part of our quality assurance framework, the modules you can choose from may vary from those 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
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:
- data analysis
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.
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
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.
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.