MSc Data Analytics

Join the information revolution.

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Overview

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

Data is everywhere. With the advance in digital and computing technology, our ability to collect and store information has grown exponentially. But so has our capacity to analyse and interpret it. With this masters you’ll build the analytical skills and techniques to unlock the secrets of big data – information that can be used to solve complex issues and drive strategic decision-making in business.

You’ll study three core units. The Computational Statistics and Visualisation unit will get you fully up to speed with the statistical techniques and software you’ll need. In the Business Intelligence unit you’ll learn how to put that software to use with real-life business data. You’ll then be ready for your Data Analytics Project, an end-to-end task that covers all the bases you’ll come across in the workplace.

Throughout it all, you’ll learn from professional staff with relevant industry experience. They’ll not only support your learning but also give you practical insights into the world of work.

It’s all about turning you into an expert. The rest is up to you. With specialist knowledge and technical skills that are valuable across a wide range of businesses and sectors, you’ll be well placed to succeed in this high-growth industry.

Non means-tested loans of up to a maximum of £10,000 will be available to postgraduate masters students - click here to find out more information

Features and Benefits

Accreditations, Awards and Endorsements

Career Prospects

Right now, according to McKinsey & Co, there are 140,000–190,000 posts in data analytics that companies cannot fill. This number is forecast to grow until 2020, with an average of 56,000 new big data jobs created each year. With a skills gap like this, the opportunities are boundless.

The advanced skills and understanding you’ll develop on our MSc course can take you into data analytics roles across a range of industries, in both the public and private sectors. You could apply for jobs like SQL data analyst, data quality analyst, insight data analyst, business intelligence analyst and statistical data analyst, to name a few.

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 School 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 School 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

Open to non-computing graduates with at least a second-class honours degree in a STEM (Science, Technology, Engineering or Mathematics) or a numerate discipline, or to applicants with a good sub-degree STEM qualification and substantial relevant work experience in a closely related area.

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

You’ll study three core topics, including Business Intelligence and Computational Statistics and Visualisation, as well as an in-depth masters project. You’ll also take two option units – allowing you to tailor your course to your interests – with subjects like Data Modelling and Analysis and Ethics, Security and Sustainability.

Core

  • Computational Statistics and Visualisation
  • Business Intelligence
  • Data Analytics Project

Options
Choose two from the following:

  • Data Modelling and Analysis then Ethics, Security and Sustainability
  • Data Modelling and Analysis then Business Analytics
  • Financial Analytics then Exploratory Programming
  • Data Management and Machine Learning

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).
Business Intelligence (with SAS)

Business Intelligence is seen as the tools/systems that play a key role in the strategic planning process of an organisation.  These tools/systems utilize statistical methods to allow the gathering, storing, accessing and analysing of data to aid in the organisations decision making process.  The aim of the unit is to provide the ability using commercial statistical software, to analyse and interpret real-life business data, thus equipping students with a structured approach to using data to identify meaningful and useful information for business analysis purposes.

  • Fundamentals of business intelligence [10%] - What is Business intelligence, what is useful data/information, structured/unstructured data/information, handling large quantities of data collected from business operations, giving/using real-life business data as examples.
  • Statistical techniques that have a real-life business intelligence application [90%]
    - Variable selection: Used to help to understand relationships between dependant and independent variables, to select the best data/transformations for objectives.
    - Multivariate Analysis Of Variance, Discriminant Analysis, Principal Component Analysis, Variable Clustering, Factor analysis.
    - Forecasting: Used to predict and simulate demand.
    - Times series analysis, Box-Jenkins methodology, Regression models with autocorrelated errors, intervention analysis and outlier analysis. ARCH/GARCH Modelling.
Data Analytics Project

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

The project will involve performing an end-to-end data analytics 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 analytics.

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.

Likely Optional Units

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.
Data Modelling and Analysis

The unit will equip students with skills and knowledge relating to the handling and analysis of data typically generated by organisations. Students will be introduced to the concept of self-service business intelligence and what impact this will have on their future career path.

The overarching theme of the unit will be to consider the core principles of business analytics: How can organisations make sense of all their data? How can data be harnessed to effectively support the decision making process? In what ways can actionable information be created and communicated?

Ethics, Security and Sustainability

The aim of this unit is to introduce students to the area of Strategic Information Systems. The core aim is to understand the nature of problems and issues faced by organisations at the operational and strategic level, and examine how Information Systems and Information Technology help to overcome such issues.

Business Analytics

Statistics is a key element of business analytics, ranging from the description of and summarising of data to advanced modelling of both cross-sectional and time series information. Students will receive a firm grounding in the most widely utilised statistical techniques in modern organisations.

The overarching theme of the unit will be to explore how various statistical techniques can aid business decision-making. Consideration will also be given to the factors driving adoption and on-going usage of management information derived from statistical analysis.

Exploratory Programming

Programming fundamentals: Control constructs, operators, procedural abstraction, simple I/O and use of libraries; Data types: primitive types, constants, variables and arrays.

Exploratory Programming environments: Integrated Development Environments; notebooks; workbenches; read-eval-print loops, interactive shells 

Financial Analytics

The aim of this unit is to provide the students with an understanding of the skills and language around financial analysis, to demonstrate examples of good and bad practice, and for the students to be able to perform financial analysis and clearly present their results.

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:

Study
Assessment

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

Fees

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: £15,500 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).

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).

Part-time students may take a maximum of 90 credits each academic year.

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

0

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

0

Postgraduate Loan Scheme

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

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Alumni Loyalty Discount

Rewarding our graduates

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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.

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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.

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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.

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