Learner Profiles
These profiles guide teaching choices; they are not entry requirements. Use the Course Overview to choose a starting point and Setup to prepare.
Profile 1: Researchers New to Programming
- Background: New to Python and moving from manual or spreadsheet-heavy analysis toward code-based workflows.
- Motivation: Build confidence with programming while improving the quality and reproducibility of data analysis.
- Needs: Clear explanations, approachable examples, and a practical route into core Python concepts.
Profile 2: Analysts Expanding into Python
- Background: Comfortable with analysis or programming in another environment, but relatively new to Python.
- Motivation: Translate existing analytical knowledge into Python-based workflows for data manipulation, visualisation, and modelling.
- Needs: A clear map of Python syntax, common libraries, and reproducible working habits.
Profile 3: Researchers Building Reproducible Workflows
- Background: Interested in using Python not just to get answers, but to structure analysis more transparently and repeatably.
- Motivation: Improve code reuse, debugging confidence, and the ability to explain analytical steps clearly.
- Needs: Good habits around functions, iteration, defensive thinking, and readable analytical code.
Put the Profiles into Practice
- For new programmers, start with the foundations and use paired prediction before running code.
- For analysts changing language, connect familiar operations to the local reference and inspect differences in indexing and object types.
- For reproducibility-focused learners, use Additional Material to check a saved workflow from a fresh session.