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Big Data Analytics MSc

Course Description

Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.

This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.

The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.

In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.

The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.

The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.

Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course. You may be able to study abroad as part of the Erasmus programme.

Key areas of study

Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research

Professional recognition

This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.

Sheffield Hallam is a member of the SAS Student Academy, the SAP Student Academy and founding member of the SAP University Alliance.

Course structure

Full time – September start – typically 12 or 18 months

Part time – September start – typically 36 months

Core modules

  • research skills and principles
  • industrial expertise
  • data integration
  • statistical modelling
  • data mining
  • handling data in the cloud
  • big data and distributed systems
  • social and economic aspects of the cloud
  • advanced statistical modelling
  • dissertation


Choose one from :

  • organisational dynamics
  • social and economic aspects of the cloud


  • essays
  • assignments
  • computer-based tests
  • practical projects
  • presentations
  • vivas


Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.

Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.

You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.

Visit the Big Data Analytics MSc page on the Sheffield Hallam University website for more details!

Entry Requirements

A good honours degree in computing, computer science, maths or statistics or other relevant areas or equivalent.

We consider your application if you do not have a relevant degree but have at least one year's direct work experience in computing or a relevant area.

Non-native speakers of English need an IELTS score of 6.0 with 5.5 in all skills (or equivalent). Please View Website for more details

Last Updated

02 August 2017

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