MSc Data Science

Unlock the power of data.

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This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible.

Data can provide the insight to shape decision-making, power scientific innovation, improve business operations or inform government policy. But it takes state-of-the-art computer science methods to unlock that power. Our MSc Data Science lets you master those methods.

With the units you’ll study, we’ll equip you with the techniques of statistical analysis and data mining that can help you find the story in complex, high-volume, high-dimensional data from various sources – formulating high-quality data models and interpreting the results. We’ll also help you put the technical skills into a real-world, professional context – helping you communicate your findings in the workplace, and address any of the legal or ethical questions that come with data processing.

Throughout the programme, you’ll have guidance from an expert team with real pedigree in the area. So what you learn in the classroom will be rooted in the kind of cutting-edge research that’s addressing societal challenges, making an impact on areas like healthcare, future cities and security.

Whether you plan to continue your studies onto PhD level or you’re looking ahead to a career as a data scientist, data architect, database developer or big data analyst – to name a few – this degree can pave the way.

Features and Benefits

Accreditations, Awards and Endorsements

Career Prospects

Between 2012 and 2015, there was a 35% increase in demand for Data Scientists in the UK. It’s a trend that looks set to continue, according to reports from McKinsey and Company, with an expected 56,000 new big data jobs created in 2019.

With the expertise you’ll develop with us, you should be well-equipped to make one of those jobs yours. You should be ready to apply for roles like:

You’ll have the specialist support of our careers service from day one, and for three years after you leave us. Between the services available through the Department of Computing and Mathematics and the University’s Career Service, you’ll have access to everything from employer events and careers fair to employability skills workshops and dedicated advisors. 

Professional Accreditation

The Department is an educational affiliate of the British Computing Society – the Chartered Institute for IT in the UK (BCS), a member of the Oracle Academy and an Academy for the Computer Technology Industry Association (CompTIA). Many of the School’s degree programmes are accredited by BCS.

The Department is also an academic partner of the Institute of Information Security Professionals who recognise our expertise in the field of information and cyber security. Mathematics degree courses are approved by the Institute of Mathematics and its Applications.

Learn more about graduate careers

Entry requirements

This course is open to anyone with a good UK honours degree – at least a 2:2 – or the international equivalent, in a STEM subject. We might also consider your application if you a have a relevant lower-level qualification, together with substantial professional experience in a related field.

International students can find out more at

Overseas applicants will require IELTS with an overall score of 6.5 with no less than 5.5 in any category, or an equivalent accepted English qualification. Accepted English qualifications can be viewed here.

Pre-sessional English courses are available to support you meeting the English language requirements. Click here for more information.

Course details

In your first term, you’ll study two units introducing you to data science, while getting you up to speed on computational statistics and visualisation. In your second term, you’ll cover two more units, based around machine learning, big data and high-performance computing. Then, in the final third of your programme, you’ll tackle an in-depth masters project.

Our research in Advanced Computational Science

Manchester Metropolitan University's Department of Computing and Mathematics is home to the Centre for Advanced Computational Science (CfACS) which conducts world‐leading theoretical and applied research in computer science, distributed across five main themes:

· Machine Intelligence

· Data Science

· Smart Infrastructure

· Human‐Centred Computing

· Computational Fluid Dynamics

Research in the School was rated internationally excellent with some rated world-leading in the 2014 Research Excellence Framework.

The Centre is characterised by its distinctive mix of expertise, research strengths and cross-discipline working. Its overarching aim is to use computer science to address societal challenges and ensure research has significant impact in areas such as healthcare, future cities and security.

This course is taught full-time over 1 year and the following units will be taken within 1 year.

  • Introduction to Data Science
  • Computational Statistics and Visualisation
  • High Performance Computing and Big Data
  • Data Management and Machine Learning
  • MSc Data Science Project

Click below for unit information.

Read more about this year of study

Core Units

Computational Statistics and Visualisation

An intensive course in data analysis primarily for non-mathematics/statistics graduates. Covers fundamentals of descriptive statistics, probability and applications.

Visualisation is a central theme and is incorporated in all three content sections.

  • Data Visualisation [20%] - Methods of sampling. Data representation - pie and bar charts; scatterplots; histograms; cumulative (relative) frequency curves; dot plots; box-whisker plots, stem-and-leaf displays. Measures of central tendency and variability for sample and grouped data. Psychological aspects.
  • Probability [30%] - Definitions and fundamental laws; counting techniques; conditional probability; Bayes theorem; the concept of a discrete probability distribution; expectations and variance; some standard discrete distributions; Geometric, Binomial, Poisson. The concept of a continuous distribution; the Normal distribution and properties; use of Normal tables. Continuous probability distributions and their properties; Expectation and variance. Some standard continuous distributions; normal and related distributions.
  • Statistical Applications [50%] - The concept of a sampling distribution; point and interval estimation; hypothesis testing; Type I and Type II errors; p values; determination of sample size; confidence intervals and significance tests for means and for proportions; single, paired and unpaired samples; Normal and t tests. F-test. Normal probability plot. Introduction to one-way Analysis of Variance. Hartley's test, Bartlett's test. Confidence intervals for treatment means and differences between treatment means. Introduction to simple linear regression. ANOVA table. Confidence intervals and prediction intervals. Correlation and rank correlation. Chi-square as a test of association and as a test of model fit. Non-parametric tests (Wilcoxon's Signed rank test, Mann-Whitney-Wilcoxon test, Kruskal-Wallis test and Friedmann test).
Data Management and Machine Learning

The aim of this unit is to develop the student’s knowledge in the areas of data management including online analytical processing; data architectures such as data warehousing and the process and application of machine learning algorithms to data.

  • Data Management Overview [15%] - Example content includes database modelling/querying (relational/noSQL), graph data modelling, applications.
  • Online Analytical Processing (OLAP) [15%] - Including the representation of multi-dimensional views of data; Technologies and Architectures; Categories of OLAP tools, Business Intelligence Tools.
  • Data Warehousing [10%] - Methodologies, architectures, modelling techniques; Data Warehousing Project Management; The Extraction, Transformational and Loading Process;
  • Machine Learning Overview [10%] - The machine learning process, Applications of machine Learning.
  • Machine Learning Algorithms [50%] - For example, artificial neural networks, naïve bayes, decision trees, clustering, association rules, text mining, fuzzy systems, application, analysis and validation.
Introduction to Data Science

Introduces data science concepts, techniques and algorithms for processing and visualising datasets so as to infer useful, actionable knowledge in domain, using Python and R as ecosystems.

  • Introduction to the data science ecosystem [10%] - basic functionality, such as fundamental data structures and operations, for specifying and running a data science pipeline; key concepts of Python (with iPython [Notebook]) and R (with RStudio).
  • Data visualization [10%] - fundamental plots: line and bar charts, histograms, scatterplots, among others.
  • Data manipulation [20%] - aspects of popular formats of datasets: tabular, text, graph, markup (e.g., XML); obtaining datasets via API requests and scraping data. Brief introduction to graph concepts: nodes, edges, paths; directed and undirected; degree. Fundamental transformational operations: extract and add features, obtain subsets of data, group and combine datasets.
  • Data analysis [20%] - summarising data with measures of totality (e.g., count, sum), central tendency (e.g., mean and mode) and spread (e.g., standard deviation). frequency and probability distributions; correlation, introductory aspects of graph analytics (e.g., centrality).
  • Data preparation [20%] - choosing, configuring and applying basic data reduction techniques and algorithms (e.g., dimensionality reduction, sampling); data cleaning, normalisation.
  • Data mining [20%] - selecting, configuring and applying common data mining algorithms for classification, clustering and regression.
High performance Computing and Big Data

The aim of this unit is to develop students' knowledge in the areas of parallel and distributed processing, machine learning approaches for handling big data,  and current parallel programming models for high-performance computing and big data processing, such as  MPI and MapReduce.

  • Current and Emerging Trends [25%] - Evaluation  of current and emerging trends underpinning parallel and distributed systems for high-performance computing and big data - paradigms and platforms, cloud computing.
  • Features of Big Data [10%] - feature extraction and dimensionality reduction approaches.
  • Artificial Intelligence [10%] - Machine learning, AI approaches and their algorithms for handling big data e.g. images, graphs, text.
  • Models and Applications [50%] - Programming models and applications for big data and High-performance computing, including MPI, OpenMP, Hadoop/MapReduce, NoSQL with case studies.
  • Professional Context [5%] - Professional, legal, ethical, social and cultural issues in high performance computing of big data.
Data Science Project

Each individual project will investigate a challenging but constrained Data Science problem.

The project will involve performing an end-to-end data science task pipeline including,  data collection, formulation of one or more questions to be asked about the data, typical preprocessing steps (e.g. cleaning, transforming and exploring), analysis, application of applicable machine learning methods, modelling, visualization, interpretation and assessment of whether models are meaningful and relevant  to the field.  Students will be required to demonstrate understanding of experimental design including validation and evaluation of models using appropriate  statistical methods.

The project will involve practical experimentation work on live data. The project may also involve practical implementation. The project will provide him or her with the opportunity to develop independent practical and analytical skills using proven methods and techniques.

Students will be able to produce well-substantiated and validated results within the limits imposed by the time constraint.  They will be able to demonstrate their investigative ability but will not necessarily be able to produce a complete piece of research or make a significant contribution to knowledge.  They will, however, be expected to critically examine their work and be able to place it in context.

Each student will be allocated a Project Supervisor from the academic staff. The main function of a Project Supervisor is to offer general advice and guidance to the student.  Students will submit a proposal to their Project Supervisor which will be scrutinised by at least one other academic member of staff.

Supporting seminars (5%), commencing before the start of the project, will be used to reinforce the students knowledge of research methods and to discuss personal organization and time management. Students need support to develop the communications and other generic skills they require to become effective researchers, to enhance their employability and assist their career progress after completing their degree.  These skills may be present on commencement or developed during the project. The need for dissertations to address, as appropriate,  legal, ethical, professional and social issues will be emphasised.

Students on the MSc will also attend a seminar which will be dedicated to examining current professional, legal, ethical, social and cultural issues in data science.

As the project is the most distinctive part of postgraduate study, there will be a strong element of personal development planning, both during the support seminars and also during the supervision sessions with individual project supervisors, as students are invited to reflect on their progress during the projects execution and write-up.

The student, at the end of the project will be required to submit a project dissertation and undertake a Viva examination to present the project work, too the Projects Supervisor and a designated Second Reader allocated by the Project Tutor.

Where it is appropriate,the project may be undertaken with an industry partner (e.g. existing employer or internship) with system creation or experimentation being work-based.

Assessment weightings and contact hours

10 credits equates to 100 hours of study, which is a combination of lectures, seminars and practical sessions, and independent study. A masters qualification typically comprises of 180 credits, a PGDip 120 credits, a PGCert 60 credits and an MFA 300 credits. The exact composition of your study time and assessments for the course will vary according to your option choices and style of learning, but it could be:


Additional information about this course

Students are expected to behave in a professional and business like manner when on placement or conducting projects with external partners.

Placement options

This MSc course is a short, intensive programme which doesn’t leave much room for a placement. But there are employers who might offer short-term internship opportunities. With our regular ‘meet the employer’ events, together with the support of our careers team, we’ll be there to help you make the most of any available opportunities. 

Department of Computing and Mathematics

Our Department of Computing and Mathematics is a vibrant community of staff and students, which prides itself on internal and external collaboration.

The department is committed to teaching and research that addresses societal challenges through disciplines like artificial intelligence, big data, computational fluid dynamics, cyber security, dynamical systems, the internet of things, smart cities, robotics and virtual reality.

More about the department

Taught by experts

Your studies are supported by a team of committed and enthusiastic teachers and researchers, experts in their chosen field. We also work with external professionals, many of whom are Manchester Met alumni, to enhance your learning and appreciation of the wider subject.

Meet our expert staff


UK and EU students

UK and EU students: Full-time fee: £8,500 per year. Tuition fees will remain the same for each year of your course providing you complete it in the normal timeframe (no repeat years or breaks in study).

Non-EU and Channel Island students

Non-EU international and Channel Island students: Full-time fee: £16,000 per year. Tuition fees will remain the same for each year of your course providing you complete it in the normal timeframe (no repeat years or breaks in study).

Additional Information

A Masters qualification typically comprises 180 credits, a PGDip 120 credits, a PGCert 60 credits, and an MFA 300 credits. Tuition fees will remain the same for each year of study provided the course is completed in the normal timeframe (no repeat years or breaks in study).

Additional costs

Specialist Costs

All of the books required for the course are available from the library. The University also has PC labs and a laptop loan service. However, many students choose to buy some of the core textbooks for the course and/or a laptop. Students may also need to print their assignments and other documents. Campus printing costs start from 5p per page. Estimated costs are £300 for a laptop up to £100 each year for books and printing.

Placement Costs


Professional Costs

Students can choose to join the BCS at any point in their study. It is not required but is useful. The annual charge is identified for every year there is also an option to take course membership for £57

Other Costs


Postgraduate Loan Scheme

Loans of up to £10,906 for many Postgraduate Courses

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Alumni Loyalty Discount

Rewarding our graduates

Learn more

Want to know more?

How to apply

The quickest and most efficient way to apply for this course is to apply online. This way, you can also track your application at each stage of the process.

Apply online now

If you are unable to apply online, you can apply for postgraduate taught courses by completing the postgraduate application form. There are exceptions for some professional courses – the course information on our on-line prospectus will give you more information in these cases.

Please note: to apply for this course, you only need to provide one reference.

You can review our current Terms and Conditions before you make your application. If you are successful with your application, we will send you up to date information alongside your offer letter.


Programme Review
Our programmes undergo an annual review and major review (normally at 6 year intervals) to ensure an up-to-date curriculum supported by the latest online learning technology. For further information on when we may make changes to our programmes, please see the changes section of our Terms and Conditions.

Important Notice
This online prospectus provides an overview of our programmes of study and the University. We regularly update our online prospectus so that our published course information is accurate. Please check back to the online prospectus before making an application to us to access the most up to date information for your chosen course of study.

Confirmation of Regulator
The Manchester Metropolitan University is regulated by the Office for Students (OfS). The OfS is the independent regulator of higher education in England. More information on the role of the OfS and its regulatory framework can be found at

All higher education providers registered with the OfS must have a student protection plan in place. The student protection plan sets out what students can expect to happen should a course, campus, or institution close. Access our current Student Protection Plan.