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Introduction
Why choose this course?
Overview
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.
Modules
Compulsory modules
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.
Optional modules
Survey Data Collection (10 credits)
Further Survey Estimation Methods (10 credits)
Applied Data Mining (10 credits)
Final Project
Compulsory modules
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.
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:
 Institute for Ethical Artificial Intelligence
 Advanced Reliable Computer Systems (ARCoS)
 Applied Software Engineering and Data Analytics (ASEDA)
 Cloud Computing and Cybersecurity group (CCC)
 Artificial Intelligence and Robotics Group (AIR)
 Visual Artificial Intelligence Laboratory (VAIL)
Entry Criteria
ENTRY REQUIRED DOCUMENTS
Home Office Share Code
For EU students only.
IF no Qualification
Please provide CV with at least 2 years of work experience, and employee reference letter.
Entry requirements
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.
To join this course you'll need a 2:2 bachelor's degree in the physical or social sciences where you have developed analytical knowledge and understanding in mathematical sciences. Typically this includes applicants with knowledge and familiarity with basic computing, mathematics and statistics concepts and methods at a bachelor's 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.
Our standard entry requirement is three A-levels or equivalent qualifications. In some cases, courses have specific required subjects and additional GCSE requirements. In addition to A-levels, we accept a wide range of other qualifications including:
 the Welsh Baccalaureate
 the Access to Higher Education Diploma
 a BTEC National Certificate, Diploma or Extended Diploma at a good standard and in a relevant subject
 the International Baccalaureate Diploma
 the European Baccalaureate Diploma
 Scottish qualifications – five subjects in SCE with two at Higher level or one at Advanced Higher level, or three subjects in Scottish Highers or two at Advanced Higher level
 a recognised foundation course
 T-levels*.
 * T-levels are a relatively new qualification but are already included in the UCAS tariff. We welcome prospective students who are taking this qualification to apply. For some programmes with specific required subjects, particular subject areas or occupational specialisms may be required.
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.
OR
An equivalent English language qualification acceptable to the University.
The entry requirement for your course will be expressed as an IELTS level and refers to the IELTS Academic version of this test. We are now also accepting the IELTS Indicator test, you can find out more about the test on the IELTS Indication site. The University however does accept a wide range of additional English language qualifications, which can be found below.
The university’s English language requirements in IELTS levels are as follows:
Course IELTS level
All other undergraduate courses 6.0 overall with 6.0 in reading and writing, 5.5 in listening and speaking
Law, Architecture, Interior Architecture, English Literature (including combined honours), English Literature and Creative Writing 6.5 overall with 6.0 in reading and writing, 5.5 in listening and speaking
Health and Social Care courses 6.5 or 7.0 overall with 6.5 or 7.0 in all components (see individual entries for course details)
Nutrition BSc (Hons) 6.5 overall with a minimum of 6.0 in each component
Built Environment Foundation,
Computing Foundation,
Engineering Foundation 6.0 with 6.0 in reading and writing, 5.5 in listening and speaking
International Foundation Business and Technology,
International Foundation Arts, Humanities and Law 5.5 overall with 5.5 in all skills
International Foundation Diploma 5.0 overall with 5.0 in all skills
If you need a student visa you must take an IELTS for UKVI test.
International Foundation Diploma (Extended pathway) 4.5 overall with 4.5 in all skills
If you need a student visa you must take an IELTS for UKVI test.
Assessment
ASSESSMENT METHODS
1. INTERNAL ENGLISH TEST if you don't have an English accredited certificate
2. Academic Interview
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.
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
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.
Career Opportunities
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.
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