Programme Specifications

Integrated Year in Industry available

Computing

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.

​The key aim of this degree scheme is to produce good quality graduates with strong skills in Computing and Statistics who are able to apply those skills to Data Science problems.

​The scheme will deliver the following outcomes:

• ​to enable students to develop the skills to be expected of any graduate, including the ability to reason logically and creatively; to communicate clearly both orally and in writing; and to be able to obtain and interpret information from a wide range of sources

• ​to equip students with the skills necessary to program in high-level computing languages

• ​to enable students to understand and apply the range of principles and tools available to the software engineer

• ​to provide students with knowledge and understanding in a range of topics in Statistics.

• ​to develop skills in the application of such knowledge and understanding to the solutions of problems in Statistics.

• ​to prepare students to work as data scientists

• ​to give students an appreciation of the professional, economic, legal and social issues surrounding software systems.

• ​to produce graduates who have the potential to succeed in a rapidly changing data science industry.​​

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

• A1 Knowledge of a range of programming languages and software design techniques

• A2 Knowledge of algorithm design and use of efficient data structures

• A3 In depth knowledge of relevant concepts and techniques of calculus, algebra, geometry, analysis, mathematical modelling, probability and statistics

• A4 An understanding of computer hardware architecture and construction

• A5 Knowledge of software engineering, the management of software projects, and their legal, social, ethical and professional aspects

• A6 Knowledge of a range of specialist topics in data science

Learning and Teaching

• ​​​Lectures (A1-A6)

• Problem classes (A2,A3)

• ​Seminars (A5, A6)

• ​Laboratory work (A1, A2, A4, A5, A6)

• ​Group and individual projects (A1, A2, A4, A5, A6)

• ​Visiting lecturer series (A5, A6)​​

Assessment Strategies and Methods

• ​​​Time-constrained examinations (A1-A6)

• ​Problem sheets (A1, A2, A3)

• ​Project diaries (A1,A5, A6)

• ​Project reports (A1, A2, A5, A6)

• ​Oral presentations (A5, A6)

• ​Computer programs and assignments (A1, A2, A5, A6)

• ​Capstone project (A1, A2, A5, A6)​​

• B1 Application of a range of concepts and principles in well-defined mathematical or statistical contexts, showing judgement in the selection and application of tools and techniques.

• B2 Implementation of computer programs in a range of modern languages

• B3 The ability to develop and evaluate logical arguments

• B4 The skill of abstracting the essential elements of problems, formulate them them in a mathematical context and obtain solutions by appropriate methods

• B5 Application of statistical principles and knowledge to practical data science problems

• B6 The capability of evaluating systems in terms of general quality attributes, possible trade-offs and risk within the given problem

Learning and Teaching

• ​​​Lectures (B1-B6)

• ​Problem classes (B1, B3, B4, B5)

• ​Seminars (B1, B4, B5, B6)

• ​Laboratory work (B2, B5)

• ​Group and individual projects (B1-B6)

• ​Visiting lecturer series (B3, B5, B6)​​

Assessment Strategies and Methods

• ​​​Time-constrained examinations (B1-B6)

• ​Problem sheets (B1, B2, B3)

• ​Project diaries (B1, B3, B6)

• ​Project reports (B1-B6)

• ​Oral presentations (B1, B3, B6)

• ​Computer programs and assignments (B1, B2, B4, B5)

• ​Capstone project (B1, B2, B4, B5)​​

10.2.2 Professional practical skills / Discipline Specific Skills

By the end of their programme, all students are expected to be able to:

• C1 Present arguments and conclusions effectively and accurately

• C2 Use computer software to analyse and interpret data

• C3 Design and carry out a survey as a member of a group, interpret the data collected and write a report

• C4 Use appropriate theory, practices and tools for the specification, design, implementation and evaluation of computer-based systems

• C5 Recognise any risks, safety or security aspects that may be involved with a computer system within a given context

• C6 Deploy effectively the tools used for the construction and documentation of data science applications
  
  Integrated Year in Industry Students have the below additional learning oucome:

• C7 Demonstrate a range of transferable skills in employment including employability, initiative, independence, and commercial awareness

Learning and Teaching

• ​​​Lectures (C1-C6)

• ​Problem classes (C1)

• ​Seminars (C1-C6)

• ​Laboratory work (C4, C6)

• ​Group and individual projects (C1-C6)

• ​Visiting lecturer series (C1, C3, C5)​​

Assessment Strategies and Methods

• ​​​Time-constrained examinations (C1, C4, C5)

• ​Problem sheets (C1)

• ​Project diaries (C2, C3, C5)

• ​Project reports (C1, C2, C4, C5, C6)​​

By the end of their programme, all students are expected to be able to:

• D1 Apply general mathematical skills to the interpretation of numerical data

• D2 Work independently

• D3 Use information technology effectively to manage information

• D4 Manage time and resources effectively

• D5 Develop effective learning skills

• D6 Be aware of the need to plan for employment and to develop various skills for such employment

• D7 Work cooperatively as a member of a software development team, recognising the different roles within a team and different ways of organising teams.

Learning and Teaching

• ​​Lectures (D1-D7)

• ​Problem classes (D1,D5)

• ​Seminars (D4, D5, D7)

• ​Laboratory work (D3, D4, D7)

• ​Group and individual projects (D2, D3, D4, D5, D7)

• ​Visiting lecturer series (D6, D7)​

• Assessment Strategies and Methods

• ​​​Time-constrained examinations (D1)

• ​Problem sheets (D1,D5)

• ​Project diaries (D3, D4, D7)

• ​Project reports (D5,D6, D7)

• ​Oral presentations (D7)

• ​Computer programs and assignments (D2, D3, D4, D5, D7)

• ​Capstone project (D2, D3, D4, D5)​​