Data Analytics MSc (PGCert PGDip)

International equivalencies

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

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. 

Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available. 

With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.

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. Through modules with substantial SAS content, students are eligible for the award of a Joint SAS Certificate.

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.

Student projects 

Recent 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

Location

Campus where this course is taught

Accommodation

Maps

Accommodation

University building

Moulsecoomb campus map