Big Data Science and Technology MSc

The modules for this course can be found in the latest programme specification.

The programme is intended to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.

Postgraduate Certificate

• Software Development (20 credits) - core
  The aim of this module is to introduce basic programming skills through a modern object-oriented programming language, design methods and tools.

• Big Data Systems and Analytics (20 credits) core
  To enable you to gain advanced knowledge and developed the skills on big data, concerning the architectures of big data systems, the management for big data projects, and computational approaches for big data analytics.

• Information Theory and Data Communication (20 credits) - optional
  The aim of this module is to introduce and apply basic principles of information theory for data communication. The focus is on concepts and techniques for reliably transmitting and efficiently processing data.

• Security, Privacy and Data Protection (20 credits) - optional
  
  
• Mobile Applications (20 credits) - optional
  To communicate knowledge of the technology necessary and available to deliver applications and web content to mobile devices.

Students will be eligible to exit with the award of Postgraduate Certificate if they have successfully completed 60 credits, to include 40 core credits at level 7, and achieved the award learning outcomes.

Postgraduate Diploma

• Statistical Data Analysis (20 credits) - core
  To acquire knowledge of statistical data analysis, statistical learning methods and data analytics techniques for the hypotheses generation and hypotheses testing, in order for making appropriate statements/predictions.

• Data Mining (20 credits) - core
  To develop a thorough understanding of the theory and practice of advanced data processing techniques. This will include description and critical evaluation of data analysis, data cleaning, data representation and data manipulation issues for mining, data processing, pattern or correlation exploration and data mining issues.

• Concurrent and Distributed Systems (20 credits) - optional
  To introduce the nature and applications of concurrent programming with typical problems requiring synchronisation of, and communication between, concurrent processes; to introduce a variety of language primitives for inter-process communication and synchronisation, and to illustrate their applications.

• Data Visualization (20 credits) - optional
  The aim of this module to provide you with key principles and techniques of data visualization. By completing this module you will gain advanced knowledge and skills on developing methods and techniques of data visualisation to improve comprehension, communication, and decision making in big data applications.

Students will be eligible to exit with the award of Postgraduate Diploma if they have successfully completed at least 120 credits to include 80 core credits at Level, and achieved the award learning outcomes.

Degree of Master

• Dissertation (60 credits) - core
  Students will be eligible for the award of Degree of Master if they have successfully completed 180 credits and achieved the award learning outcomes.