Data Analytics MSc (PGCert PGDip)

How this course is delivered

Students have a blend of on-campus and digitally enabled remote learning that keeps everyone safe, connected and involved in university life. Lots of opportunities are provided to interact and engage with lecturers and other students. The balance between online and on-campus learning varies by module, and in response to the external environment.

Course structure

The course covers three main areas:

• data management – structuring and manipulating data for analysis purposes
• data interpretation – statistical analysis using advanced features of industry-standard software such as SAS, SPSS and R
• project management – the business-specific and strategic aspects of analytics.

You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques. 

Our mathematics department has SAS Academy status. SAS is one of the largest providers of business analytics software in the world. Academy status gives our students the opportunity to meet SAS customers and their industry partners at careers fairs, to train in skills needed by a wide range of companies and organisations, and to be prepared for SAS certification exams. 

Syllabus

You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules allowing you to focus on particular areas of interest.

Core modules

• Data Management – provides an understanding of contemporary database management systems. Explores a methodology for database design and development, and develops skills in searching, reporting and analysing the data. Topics covered include database implementation and administration, data modelling and business intelligence.
• Programming for Analytics – provides competencies in computer programming and algorithm design with emphasis on statistical programming and data analysis. The module covers both general issues of algorithm design and data structures and implementation issues in R and SAS.
• Data Visualisation and Analysis – covers principles of data visualisation and specialised tools for data visualisation and analysis such as SAS Visual Analytics and Qlikview. The module also explores the mathematical and statistical theory behind data analysis.
• Business Analytics Strategy and Practice – develops analytics-specific project planning concepts within this context, enabling students to design and manage analytics projects and present the business case to senior management.
• Industry project – substantial, independent project undertaken with the supervision of a member of the teaching team. Projects are normally industry-based using real data sets.

Option modules*

• Multivariate Analysis and Statistical Modelling – design statistical experiments, analyse multivariate data and apply classical and modern statistical modelling techniques. Enhances skills in the use of specialist software such as R, SPSS or SAS.
• Stochastic Methods and Forecasting – an understanding of stochastic models and their applications in a business context. The module also covers forecasting methods with the emphasis on selecting the best forecasting method for a business problem and correct application of that method.
• Data Mining and Knowledge Discovery in Data – find useful and relevant patterns, trends and anomalies in data sets, and summarise them in a form which may be used to support enterprise decisions – one of the great challenges of the information age. Emphasis is on the big, real-world picture rather than inside-the-box systems design engineering details. 
• Risk Analysis and Retail Finance – introduction to the statistical methods used to estimate risk and reward in retail credit. The focus is on retail finance especially the provision of credit and lending services.
• Medical Statistics – introduction to the methods originally designed for clinical trials and now being used in other contexts including sociology and marketing research. Topics include assessment of risk factors, comparing treatments and assessing survival data.

*Option modules are indicative and may change, depending on timetabling and staff availability.

Placement year

To further increase your employability and international experience we offer full-time students starting in September the opportunity to take a placement year. This means that your course will take two years, but you’ll be graduating with work experience.

The year on placement in a role related to your course will allow you to put all that you have learned on your masters into practice, enhancing your career opportunities and giving you practical experience in your area of expertise.

The placement is assessed formatively throughout the year, giving you the chance to reflect on your experience and helping to build your CV.

Work placements are optional, and you'll arrange your placement with support from our dedicated Placements and Employability team.

We offer a two-year international study visa route to support applications for this course.

Student projects 

Examples of student projects include:

• Predicting patterns in relation to data involved in mobile gaming

• Factors affecting visitor behaviour and visitor numbers to the Royal Pavilion, Brighton

• The effects of Tommy John surgery on major league baseball pitchers

• Automatic classification of students’ free-text responses in NSS and BSS surveys

Staff profile

International requirements and visas

We can help you meet our English language or academic entry requirements.

Visas and immigration advice

Course fees

Scholarships, bursaries and loans

We offer a range of scholarships for postgraduate students. Bursaries and loans may also be available to you.

Find out more about postgraduate fees and funding.

What's included

You may have to pay additional costs during your studies. The cost of optional activities is not included in your tuition fee and you will need to meet this cost in addition to your fees. A summary of the costs that you may be expected to pay, and what is included, while studying a course in the School of Computing, Engineering and Mathematics are listed here.

• You will have access to computers and necessary software, however many students choose to buy their own hardware, software and accessories. The amount spent will depend on your individual choices but this expenditure is not essential to pass any of our courses.
• In most cases coursework submissions are electronic but students may wish to print notes which would involve an extra cost.
• Costs of up to £50 are included in the fees for students on engineering courses to pay for materials for their final year projects. On rare occasions where material costs exceed £50, they will need to be paid for by the student.

You can chat with our enquiries team through the Stay in touch panel at the end of this page if you require further information. Or check our finance pages for advice about funding and scholarships as well as more information about fees and advice on international and island fee-paying status.

Info

The fees listed here are for full-time courses beginning in the academic year 2021–22.

Further tuition fees are payable for each subsequent year of study and are subject to an annual increase of no more than 5% or RPI (whichever is the greater). The annual increase for UK students, who are subject to regulated fees, will increase no more than the statutory maximum fee.

You can find out more about our fees in the university's student contract and tuition fee policy (pdf).

The tuition fee you have to pay depends on a number of factors including the kind of course you take, and whether you study full-time or part-time. If you are studying part-time you will normally be charged on a pro rata basis depending on the number of modules you take.

Local area

Campus where this course is taught

Accommodation

Maps