Schedule

Day 1 and Day 2 run from 09:15–16:00. On Friday, Day 3 runs from 11:00–16:00. Each teaching block can contain a short talk with slides, live coding, discussion, and hands-on work in VS Code. The times show the broad shape of the day rather than every transition.

Day 1: Research-ready Python and neuroscience arrays

Time	Block	Main content
09:15–10:30	Welcome and research-ready Python	Python across the MSc courses, retrieval warm-up, environments, interpreters, kernels, projects, and paths
10:30–10:45	☕ Break
10:45–12:15	Python foundations in practice	Objects, methods, attributes, dictionaries, mutation, functions, documentation, and Notebook 0: Python warm-up
12:15–13:00	🍽️ Lunch
13:00–14:20	NumPy and EEG data	Dimensions, axes, epoching, evoked responses, shape predictions, and Notebook 2: EEG arrays
14:20–14:35	☕ Break
14:35–15:45	Visualisation and figure remix	Matplotlib objects, uncertainty, figure critique, and paired work with the Stroop dataset
15:45–16:00	Day 1 close	Questions, recap, and a preview of Day 2

Day 2: Behavioural data, models, and transfer

Time	Block	Main content
09:15–10:30	Functions and analysis pipelines	Retrieval from Day 1, side effects, assertions, documentation, and explicit processing steps
10:30–10:45	☕ Break
10:45–12:15	Behavioural data with pandas	Trial-level data, inspection, filtering, transformations, grouping, visualisation, and the code-commenting exercise in Notebook 1: Lexical decision data
12:15–13:00	🍽️ Lunch
13:00–14:15	From data to models	X, y, .fit(), .predict(), pipelines, leakage, and NLP representations in Notebook 3: Model workflow and Notebook 4: NLP text features
14:15–14:30	☕ Break
14:30–15:40	NLP and Data Science project	Group work creating a structured project, writing a runnable text-classification script, and documenting the analysis
15:40–16:00	Transfer and workshop close	Examples from ACN, NLP, and Data Science, followed by discussion and next steps

Day 3: Learning, skills, and AI

Day 3 is an open conversation about learning, expectations for the Master’s degree, the courses ahead, and what students want to learn. Most of the time is spent talking together rather than working in a notebook.

Time	Block	Main content
11:00–11:30	Opening conversation	What do we want to learn during the Master’s degree?
11:30–12:15	Learning and skills	What does it mean to understand something, and which skills do we want to build?
12:15–13:00	🍽️ Lunch
13:00–14:00	Talking about study	Share experiences of learning and discuss what helps us work independently
14:00–14:15	☕ Break
14:15–15:00	Courses and expectations	Look ahead at the courses and discuss what we expect from ourselves
15:00–15:40	Open group conversation	Follow the questions that matter to the group, including the role of AI in studying if it comes up
15:40–16:00	Closing conversation	What do we want to learn and carry into the semester?

What we deliberately leave out

Two days cannot cover everything. We do not teach custom class hierarchies, decorators, async Python, transformer architecture, neural-network mathematics, or specialised neuroimaging packages. We build the Python fluency those topics depend on.

Day 3 in practice

Day 3 is mainly an open conversation about learning, expectations for the Master’s degree, and the courses ahead. We will talk about what students want to learn, which skills they want to build, and how they want to approach the start of the programme. There is no fixed set of answers; the discussion can follow the questions that matter to the group.