# 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.