BSc Data Science

Year 1

(Award available for year: Certificate of Higher Educ)

Learning outcomes

1. Articulate the main aspects of the interdisciplinary, integrated and iterative nature of data science, the data science lifecycle, and the strategic organisational role of a data scientist.
2. Explain and apply basic techniques of Calculus, Algebra and Statistics to solve seen and unseen problems.
3. Curate, process, present and evaluate basic examples of qualitative and quantitative data.
4. Explain and apply basic techniques of functional and procedural programming e.g. for the construction of simple algorithms, the investigation of basic data science problems, and the processing of big data.
5. Individually and collaboratively apply computer programming and other digital tools to creative problem solving, computational modelling, and communicating the data analysis.
6. Analyse and construct mathematical and data science-related arguments logically, carefully identifying assumptions, scope and limitations.
7. Apply basic principles of the science of cognition, collaboration and communication to your own learning practice, to working in a team, and to communicating data science content to different audiences e.g. through data storytelling and visualisation.
8. Explain the central role of ethics in data science, and identify and discuss ethical challenges concerning communication, biases, personal data, and data science in societal and commercial contexts.
9. Use information searching tools appropriately in order to create logically structured introductory content e.g. for essays, blogs, bibliographies, reports, posters and presentations.
10. Articulate the importance of reproducibility, transparency and collaboration in data science, and practise good academic integrity behaviours, correctly attributing own and other people’s work.
11. Select an appropriate range of skills gained in different disciplinary contexts in order to investigate data science questions collaboratively in interdisciplinary ways, and to articulate the importance of blending domain knowledge, behavioural sciences and technical skills for impactful data science projects.
12. Articulate similarities and differences between different iterative cycles of enhancement such as the data science lifecycle, the scientific method, evolution, design, and reflective practice, and explain the central role of optimisation in data science.
13. Monitor and analyse your own learning process, identifying and reflecting upon the key data science knowledge, skills and behaviours that you have developed with reference to professional competency frameworks.

Skills Learning Outcomes
SLO1. Communicate and visualise data science content for diverse audiences using data storytelling.
SLO2. Work effectively in teams to produce an outcome.
SLO3. Develop strategies for real-world scenarios and organisational contexts around ethics, uncertainty, design, interdisciplinarity and behavioural sciences.
SLO4. Practise an active, reflective and strategic approach to learning and 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.