BSc Data Science and AI
Entry requirements
The standard minimum entry requirements for this course are: Grades BCC or above at A-level Merit Merit Merit (MMM) at BTEC Extended Diploma (preferred subjects include Computer Science and ICT, or Design and Technology) Access to Higher Education Diploma with 104 UCAS tariff points (preferred subjects include Computer Science and ICT, or Design and Technology) Equivalent EU/International qualifications, such as International Baccalaureate Diploma. Grades CC or above at A-level Merit Pass Pass (MPP) at BTEC Extended Diploma (preferred subjects include Computer Science and ICT, or Design and Technology) Access to Higher Education Diploma with 64 UCAS tariff points (preferred subjects include Computer Science and ICT, or Design and Technology) Equivalent EU/International qualifications, such as International Baccalaureate Diploma You should also have three GCSE passes at grade 4 or above (grade A*-C). If you do not have a Science or Mathematics-based A-level, you should have at least Grade B/Grade 6 at GCSE Mathematics.
About this course
BSc (Hons) in Computer Science. Core topics include Programming, Systems Design, Computer Architecture, Embedded Systems, Data Analysis, Statistics, Project Management, Policy Analysis, Entrepreneurship, and Artificial Intelligence & Machine Learning. The curriculum includes a final year thesis project.
Modules
- Year 0 (common with BSc Computer Science)
- Methods 00: Programming and Computational Thinking:
- This unit establishes the core theory and practice of programming. You will study the syntax and meaning of industry-standard languages and learn to configure and use professional tooling and development environments. The unit integrates the basic mathematics necessary for logical problem-solving and examines the importance of visual aesthetics in creative software output. Furthermore, you will learn how to effectively utilise technical documentation and access support networks and developer communities to solve technical problems.
- Critical 00: Optimistic Futures for Computing:
- Critical Project 00: The Human Computer Relationship:
- Methods 01: Form and Structure:
- Critical 01: Real World Patterns in Computing:
- Critical Project 01: Building Human Computer Relations:
- Methods 1: Introducing Computer and Data Science:
- Critical 1: Mathematics and Statistics for Data Science:
- Critical Project 1: Data, Representation and Visualisation:
- Methods 2: Data Applications in Wearable and IoT:
- Critical Project 2: Database Systems for Data Science: Hybrid and Cloud Integration:
- Critical 2: Data Governance, Privacy Compliance and Computational Ethics:
- Methods 3: Algorithms and Complexity:
- Critical 3: Organisations and Computing Entrepreneurship:
- Critical Project 3: Data Science Project: Software Development with Database Integration:
- Methods 4: Deep Learning and Big Data Integration: Technologies and Tools:
- Critical 4: Machine Learning for Data Science and Ethical Computing:
- Critical Project 4: Data Science Project: Software Development with Big Data and Cloud:
- Methods 5: Artificial Intelligence and Advanced Analytics:
- Critical 5: Data and Cybersecurity:
- Critical Project 5: Applied Analytics: Data-driven Product Development:
- Critical 6: Responsible Data Science: Equity, Consent, Innovation and Ethics:
- Critical Project 6: Final project:
- Diploma in Professional Studies (Optional year)