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Data Analytics

MSc

Key facts
Overview
How to apply
Tuition fees
Learning and assessment
Research
After you graduate

Key facts

Start dates

September 2020 / September 2021

Location

Wheatley

Course length

Full time: 12 months

Part time: 2-5 years

Department

School of Engineering, Computing and Mathematics

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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.

Male student at computer

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.

International qualifications and equivalences

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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

Apply now

Tuition fees

Please see the fees note
Home/EU full time
£9,450
Home/EU part time
£525 per 10 credit module
International full time
£15,700
Home/EU full time
£535 per 10 credit module
Home/EU part time
£535 per 10 credit module
International full time
£16,300

Questions about fees?

Contact Student Finance on:

financefees@brookes.ac.uk

Tuition fees

2020 / 21
Home/EU full time
£9,450
Home/EU part time
£525 per 10 credit module
International full time
£15,700
2021 / 22
Home/EU full time
£535 per 10 credit module
Home/EU part time
£535 per 10 credit module
International full time
£16,300

Questions about fees?

Contact Student Finance on:
+44 (0)1865 483088
financefees@brookes.ac.uk

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

For general sources of financial support, see our Fees and funding pages.

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).
Male and female students taking notes on laptops

Study modules

Taught modules

Compulsory modules

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 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.

Introduction to Survey Research

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

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 you to time series and forecasting methods.

Research and Study Methods

This module will equip you with the skills necessary to perform research and employ effective study methods which will underpin your dissertation.

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.

Final project

Compulsory modules

Dissertation

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 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 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.