| | Learning outcome |
|---|
| 1 | Exhibit a broad and coherent body of knowledge in data science, and be able to describe the relationships between context-specific knowledge and data and evaluating how these can guide data analytics |
|---|
| 2 | Exhibit deep knowledge of the underlying concepts and principles of experimental design, analysis and data outputs, of the relationships between these concepts, and of potential pitfalls |
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| 3 | Use quantitative models or visualisation methods on multiple types of data |
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| 4 | Manage data, metadata and derived knowledge, using appropriate storage, access and administration tools |
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| 5 | Communicate concepts and findings in data science through a range of modes for a variety of purposes and audiences, using evidence-based arguments that are robust to critique |
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| 6 | Identify analytical approaches appropriate to a specific problem in data analysis, simulation-based modelling or equation-based modelling |
|---|
| 7 | Create and use databases and graphical information systems using programming skills |
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| 8 | Address authentic problems in data science, working professionally and ethically and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams |
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