MSc Big Data Analytics

All our courses are designed around key principles based on engaging you with the world, collaborating with others, challenging you to think in new ways, and providing you with a supportive environment in which you can thrive.

This course explores statistics, computing, and management to help put you at the forefront of this vitally important field. You will gain an insight into Data Analytics, including Statistical Modelling and Data Mining, Big Data and Distributed Systems, Data Integration, Programming Concepts and Practice, and the core skills around professionalism, project management, and research.

Our partnerships with businesses inform the course design, ensuring the content is relevant, up-to-date, and meets the needs of the industry. These partnerships also enable the inclusion of cutting-edge software such as SAS, Python, RStudio, and others.

You learn through

• teaching sessions
• learning materials
• recommended readings
• problem-based learning
• group projects
• contemporary software platforms

This course is delivered face-to-face over three semesters - teaching is scheduled based on the length of your course and the academic calendar.

Applied learning

Work placements

You will have the opportunity to go on a work placement for up to 12 months starting during the summer of your second year. You are supported in finding placements and can apply for a vacancy as you would a job. There are dedicated sessions to support you in CV writing, interview skills, and job applications. Previous students secured placements in roles such as

• data analysts
• business analysts
• credit risk analysts
• data scientists
• digital data analyst
• data engineer
• management information analyst.
• Live projects

You will have the opportunity to work on real work-based projects supplied by an external company or emulated industry case studies. You apply the skills and knowledge gained from your studies in topics such as data integration, statistical modelling, big data, and distributed systems to situations of genuine complexity.

Networking opportunities

The teaching team makes use of their research and industry connections to facilitate the inclusion of real work scenarios and case studies within many of the modules.

Many industries make use of the skills developed on the course once you have graduated, including:

• Pharmaceuticals
• Finance
• Marketing
• Retail

Public and Private Sector organizations such as the Police, Government, NHS, Amazon, Facebook, the UK Ministry of Defence, HSBC, and GSK all make use of data management/analytics extensively to help t to better understand their customers and to make informed business decisions. IBM ranked these jobs as one of the most difficult for employers to recruit, due in part to the shortage of skills in this area.

Modules

Module and assessment information for future years is displayed as currently validated and may be liable to change. When selecting electives, your choices will be subject to the core requirements of the course. As a result, selections may be limited to a choice between one of two or more specified electives in some instances.

Final year

Compulsory modules

• Advanced-Data Management Project: 45 credits
• Data Analytics: Tools And Techniques: 30 credits
• Dissertation For Computing: 45 credits
• Industrial Expertise: 15 credits
• Programming Concepts And Practice: 15 credits
• Research Skills For Computing: 15 credits
• Study Skills And Project Management: 15 credits

This course prepares you for a career in

• statistical analysis and machine learning techniques
• database and programming technologies
• statistical theory
• marketing and insight
• credit risk
• data and technology
• digital analytics
• data science

Previous graduates of this course have gone on to work for the following companies

• Bank of England
• OCF
• HSBC
• INC Research
• Tata Consultancy Services
• HM Revenue and Customs
• Axis Bank

Some of the associated careers in these fields are

• customer insight analyst
• credit risk analyst
• fraud analyst
• forensic analyst
• big data architect
• business intelligence developer
• digital analyst
• data scientist