Programme Specifications

QAA Subject benchmark statements for degrees in Computing (including masters)

September 2023

Data Science is a rapidly growing area of Computing specialism, with applications in all areas of business, of government, and of science. Applications range from identifying customers buying patterns to tracking the spread of a disease, from monitoring expensive machinery to logging and improving an individual's health. There is a huge demand for graduates with skills in ‘Big Data’: data management, statistical techniques and machine learning for data analysis and data mining.

This programme:

• equips students with the skills in quantitative analysis, data mining, and data presentation that they need to operate as a data scientist.

• develops the ability to program sufficiently to be able to filter and rearrange data automatically

• enables students to perform data analysis and modelling

• gives an understanding of information security issues, and the legal, social, ethical and professional issues involved in handling personal data

• provides the experience of a practical data handling project

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

• Apply appropriate programming and data modelling techniques to track and manage large volumes of data

• Apply advanced statistical methods and tools: Opportunities will be provided to design statistical experiments and to use statistical packages such as R to analyse data.

• Critically evaluate the application of specific statistical techniques

• Effectively interpret and report the results of analyses:

• Demonstrate research skills, including bibliographic and dissemination skills

• Identify potential security issues with massive data volumes and high transaction rates Identify and make recommendations to address issues of persistence, resilience, security and verification of systems that process large volumes of data, as well as legal, social, ethical and professional issues in data handling

• Develop analytical applications for conventional and emerging data management systems.

• Demonstrate ability to complete a substantial project where a degree of research is required

Teaching and Learning:

Teaching and learning will be by way of a combination of conventional lectures, seminars and laboratory practicals.

Assessment: A variety of assessment methods will be used, including presentations, written reports and practical and conventional examinations.

10.2.1 Intellectual skills

• Evaluation of machine learning and statistical techniques with respect to specific problems

• Reflection on specific ethical, professional and legal issues

Teaching, learning and assessment methods used to enable outcomes to be achieved and demonstrated.

Teaching and Learning: Seminars, practical lab sessions

Assessment: Presentations, written reports, conventional written examinations

10.2.1 Professional practical skills

• Programming

• Design and implementation of conventional and emerging data storage solutions

• Statistical techniques

• Experimental design

• Machine learning techniques

Teaching, learning and assessment methods used to enable outcomes to be achieved and demonstrate.

Teaching and Learning: Seminars, practical laboratory sessions

Assessment: Assessed coursework, conventional written examinations, practical examinations

• Written and oral presentation skills

• Critical thinking

Teaching and Learning: Seminars, assessed coursework

Assessment: Assessed coursework, conventional written examinations