MSc Big Data Analytics (Online)

Further your computer science career with a specialist postgraduate degree in big data analytics.

This online master’s programme has been designed to equip students with expertise in an area of computing that has seen recent and rapid growth, and in which there is expected to be a significant skills shortage.

You will have the opportunity to gain a comprehensive understanding of both the technology that supports big data analytics and the practical application of this technology in the context of business information and real-world problems.

To achieve a full master’s degree, you will be required to complete 180 credits. This programme is also available as a postgraduate diploma (PG Dip) which amounts to 120 credits and a postgraduate certificate (PG Cert) which amounts to 60 credits. Students who complete the PG Cert and PG Dip will have the opportunity to progress to a full master’s degree.

The programme is accredited by the BCS, The Chartered Institute for IT, for the purposes of meeting the further learning academic requirement for registration as a Chartered IT Professional.

Why study this subject? 

The Big Data Analytics MSc follows a career-driven curriculum, developed to provide practical skills and knowledge directly applicable to a workplace. Throughout your studies, you will explore a wide range of programme modules.

The MSc programme is accredited by the BCS, The Chartered Institute for IT, for the purposes of meeting the further learning academic requirement for registration as a Chartered IT Professional.

Modules

• Global Trends in Computer Science (15 credits)
• Data Visualisation and Warehousing (15 credits)
• Machine Learning in Practice (15 credits)
• Cloud Computing (15 credits)
• Security Engineering and Compliance (15 credits)
• Deep Learning (15 credits)
• Elective module: choose one:
  
  • Applied Cryptography (15 credits)
  • Cyber Forensics (15 credits)
  • Cybercrime Prevention and Protection (15 credits)
  • Information Technology Leadership (15 credits)
  • Multi-Agent Systems (15 credits)
  • Natural Language Processing and Understanding (15 credits)
  • Reasoning and Intelligent Systems (15 credits)
  • Robotics (15 credits)
  • Security Risk Management (15 credits)
  • Strategic Technology Management (15 credits)
  • Technology, Innovation and Change Management (15 credits)
• Research Methods in Computer Science (15 credits)
• Computer Science Capstone Project (60 credits)

Teaching methods and style

This programme is designed to be studied wholly online and part-time. Teaching is delivered through our state-of-the-art Virtual Learning Environment (VLE), which provides students with access to all resources required for interactive study online. On this platform you will be encouraged to work collaboratively with classmates and actively read around your topic through our comprehensive library of eBooks and journals.

Methods of assessment 

Assessment is exclusively through online assignments rather than examinations. You will be assessed through a range of activities, including written assignments, presentations, discussion forum participation and journal entries.

All applications will be considered on a case-by-case basis. If you want to discuss your previous qualifications and experience before applying, please contact our admissions team.

Applicants should possess either:

• A minimum of a 2:2 class degree in Computer Science or a closely related subject, equivalent to a UK bachelor’s degree, coupled with two years’ experience in employment; or
• Professional work experience and/or other prior qualifications, which will be considered on a case-by-case basis.

All applicants must provide evidence that they have an English language ability equivalent to an IELTS (academic) score of 6.5.

The programme follows a career-driven curriculum, developed by industry leaders and experts to ensure the taught skills and knowledge are directly applicable to a workplace. Graduates will be able to successfully apply their newly acquired skills and knowledge in demanding roles such as Data Scientist, Big Data Consultant, and Machine Learning Engineer.