BSc Data Science

Year 2

(Award available for year: Diploma of Higher Education)

Learning outcomes

1. Apply object-oriented programming principles to computational modelling in statistical or algorithmic paradigms, following professional software engineering practices.
2. Apply sound design and project management practices for the collaborative creation of code to solve user problems, taking into account organisational strategic goals or business constraints.
3. Confidently implement algebraic ideas computationally in order to develop key data science tools and techniques, and articulate the fundamental role algebra plays in data science.
4. Manage data-driven research projects by identifying logical steps and milestones and combining independent with group work.
5. Explain and apply algebraic principles of graph theory and network analysis to the analysis of small and large networks.
6. Explain how complexity arises in real-world systems, identify key features of complexity, e.g. in sustainability and data science contexts, and articulate the importance of domain knowledge and interdisciplinary modes of working in order to have impact in real-world settings.
7. Select appropriate statistical paradigms and competently apply key statistical techniques to the solution of data science problems, clearly identifying assumptions and limitations of the approach.
8. Select appropriate optimisation and machine learning tools and techniques to the analysis of data science questions.

Skills Learning Outcomes
SLO1. Reflect on the challenges of communicating across multi-disciplinary interfaces connecting software engineering, mathematical modelling, statistics, marketing, non-specialist publics and decision makers.
SLO2. Use effectively appropriate digital methodologies such as software, programming languages or libraries in order to solve problems in data curation, modelling and visualisation.
SLO3. Identify, analyse and reflect upon the key knowledge, skills and behaviours that are being developed in relation to professional competency frameworks and set strategic goals for development.

Assessment

The majority of the data science modules will be assessed through a combination of individual and collaborative coursework in a variety of formats. The majority of the mathematics modules will be assessed through a combination of in-person exams and coursework assignments. The form of the coursework will vary from module to module but typically would involve solving sets of problems which may or may not require the use of computer packages or computer programming. Project-based modules will be assessed through written submissions and presentations.