Python across the MSc courses

Python across the MSc courses#

Python is a shared research tool across the programme rather than a topic that belongs to only one course. The same language constructs reappear in different scientific settings.

This workshop primarily prepares you for three courses:

Advanced Cognitive Neuroscience (ACN), where Python supports loading, organising, transforming, averaging, and visualising multidimensional neuroscience data.
Natural Language Processing (NLP), where Python is used to represent text, build processing pipelines, create numeric features, and interact with machine-learning models.
Data Science, Prediction and Forecasting, where Python supports tabular data analysis, statistical learning, model evaluation, and reproducible prediction workflows.

What is shared?#

All three courses require you to move between three levels of understanding:

Python level: What does this expression do?
data level: What type, shape, columns, or keys does the object have?
scientific level: What does this transformation mean for the research question?

For example:

result = data.mean(axis=0)

At the Python level, this calls a method. At the data level, it removes an axis by averaging. At the scientific level, its meaning depends on whether axis 0 represents participants, trials, documents, or something else.

Advanced Cognitive Neuroscience#

Typical tasks include handling participant files, understanding library objects, working with epochs, averaging signals, and plotting results.

Foundation	Why it matters in ACN
`pathlib` and file patterns	Locate participant, condition, and recording files safely
Environments	Install compatible versions of neuroscience packages
Objects and classes	Read APIs in which recordings, epochs, and results are objects
Dictionaries	Store metadata, parameters, event mappings, and channel information
NumPy arrays	Represent data such as trials × channels × time
Axes and aggregation	Understand epoching, baseline correction, and evoked responses
Matplotlib	Inspect signals and communicate comparisons

The most important mental habit is to annotate every array dimension with its scientific meaning.

Natural Language Processing#

Typical tasks include cleaning text, tokenisation, corpus exploration, vectorisation, and using learned representations.

Foundation	Why it matters in NLP
Strings and lists	Represent documents, sentences, and tokens
Dictionaries	Represent documents, labels, vocabularies, and model inputs
Loops and comprehensions	Transform collections of documents
Functions	Make preprocessing steps reusable and testable
pandas	Explore corpora and metadata
Arrays and tensors	Represent documents × features or batch × tokens × embeddings
Objects and methods	Use vectorisers, tokenisers, pipelines, and models

An NLP pipeline often begins with text and ends with numbers. At every step, ask what was preserved, removed, or encoded.

Data Science, Prediction and Forecasting#

The course covers statistical learning, regression, classification, resampling, regularisation, non-linear models, trees, support vector machines, and deep learning. Python is the working language that connects the data to those ideas.

Foundation	Why it matters in Data Science
pandas	Clean, join, summarise, and inspect tabular data
NumPy	Understand numeric representations and dimensions
Functions	Build repeatable transformations and evaluation procedures
Objects and methods	Read the common `fit`, `predict`, and `transform` API
Boolean logic	Filter observations and define conditions correctly
Environments and seeds	Make analyses reproducible
Visualisation	Diagnose data and communicate model behaviour

The central data structure is usually a feature matrix X with shape samples × features, accompanied by a target y.

A shared example#

These expressions come from different domains but use the same Python ideas:

# ACN: trials × channels × time → channels × time
evoked = epochs.mean(axis=0)

# NLP: documents → document-term matrix
X_text = vectorizer.fit_transform(documents)

# Data Science: samples × features → fitted estimator
model.fit(X_train, y_train)

In each case, identify:

the object on the left of the dot;
the method being called;
the arguments supplied;
the type and shape of the returned object;
the scientific interpretation of the transformation.

What this workshop covers#

The two days progress from language fundamentals to research workflows:

values and types
    ↓
collections and control flow
    ↓
functions, objects, and files
    ↓
arrays, tables, and visualisation
    ↓
reproducible analysis and model workflows

You are not expected to memorise every method. You are expected to know how to inspect an object, consult documentation, test an assumption, and explain what your code does.