About the course
Course content
This course is suitable for numerate graduates across many disciplines. Non-computing graduates are eligible.
You will learn how to use state-of-the-art computer science methods to process a range of data, including (but not limited to) big data. You will develop technical, statistical, analytical and data mining skills. The course incorporates methods of statistical analysis and data mining to extract understanding from data, formulate high-quality data models and interpret them to ‘tell a story’. You will learn how to communicate these effectively to stakeholders, bearing in mind ethical, legal and societal implications.
You will be introduced to data science concepts, techniques and algorithms for processing and visualising datasets so as to infer useful, actionable knowledge.
Features and benefits of the course
-The School has an extensive range of equipment in our own specialist laboratories which is supported by a dedicated team of technical staff.
-Research in the School was rated 'internationally excellent' with some rated 'world-leading' in the 2014 Research Excellence Framework (REF).
-Our online virtual learning platform Moodle, provides access to lectures, course materials and assessment information.
-Classes are concentrated on certain days of the week to facilitate part-time students’ attendance and allow full-time students to undertake part-time employment if necessary.
-The School of Computing, Mathematics and Digital Technology is a member of the Oracle Academy.
-We are an academic partner of the Institute of Information Security Professionals (IISP). This partner status recognises our expertise in the field of information and cyber security.
-We are also an Academy of the Computer Technology Industry Association (CompTIA) and deliver their partner programme which provides a pathway for students towards a rewarding, high-growth IT career.
Placement options
Some students undertake practical work for their projects while working in organisations which have offered placement opportunities.
About the Course
The School has an extensive range of equipment in our own specialist laboratories which are supported by a dedicated team of technical staff, including GPU clusters to support big data processing.
Classes are concentrated on certain days of the week to facilitate part-time students’ attendance and allow full-time students to undertake part-time employment if necessary.
Your individual project will investigate a challenging but constrained data science problem. The project will involve performing an end-to-end science task pipeline including data collection, formulation of one or more questions to be asked about the data, typical preprocessing steps, for example, cleaning, transforming and exploring, analysis, applicable learning methods, modelling, visualisation, interpretation and assessment of models.
Assessment details
Assessment will be through coursework, examination and dissertation.