Campus AI · Sciences Foundation. Python, data and AI for science students.

Where science graduates win in the AI economy: data, research, biotech and healthcare.

Topics covered

• Where science graduates win in the AI economy
• Career paths in data, research, biotech and healthcare
• The skills employers look for in AI-augmented science roles
• Choosing the Sciences specialisation to take next

You produce: a map of AI-augmented careers in your subject.

Variables, functions, loops, files and libraries: enough to be productive in a week.

Topics covered

• Variables, data types and operators
• Functions, loops and conditions
• Reading and writing data files
• Installing and importing libraries in Jupyter and Google Colab

You produce: Python scripts that read and process a data file.

DataFrames, filtering, groupby and merging: the library you will use every day.

Topics covered

• Loading data into DataFrames
• Filtering, sorting and selecting data
• Grouping and summarising with groupby
• Merging and joining datasets

You produce: a cleaned, analysis-ready dataset.

Arrays, vectorisation and basic numerical methods.

Topics covered

• NumPy arrays and array operations
• Vectorisation: fast calculations without loops
• Basic numerical methods for scientific problems
• Using NumPy with Pandas in a scientific workflow

You produce: a numerical analysis notebook.

Publication-quality plots, and knowing what to plot and what not to.

Topics covered

• Line, bar, scatter and histogram plots in Matplotlib
• Statistical plots with Seaborn
• Choosing the right chart, and what not to plot
• Labelling and exporting figures for reports and papers

You produce: a set of publication-quality figures.

Descriptive statistics, hypothesis testing and regression, practical rather than theoretical.

Topics covered

• Descriptive statistics: averages, spread and distributions
• Hypothesis testing and p-values in practice
• Linear regression with Python
• Reading and reporting statistical results

You produce: a statistical analysis of a real dataset.

Use ChatGPT and Claude for code, debugging, explanation and literature.

Topics covered

• Writing and fixing code with ChatGPT and Claude
• Getting clear explanations of methods and concepts
• Searching and summarising scientific literature
• Checking AI output for errors and invented references

You produce: an AI-assisted workflow for your own coursework.

An introduction to supervised learning, evaluation metrics, and when to use machine learning.

Topics covered

• What supervised learning is, and where it fits in science
• Training a first classification or regression model
• Evaluation metrics: accuracy, precision, recall and error
• When to use machine learning, and when not to

You produce: a first supervised learning model, evaluated.

Draft abstracts, methods sections and literature reviews with AI.

Topics covered

• Drafting abstracts with AI
• Writing clear methods sections
• Structuring a literature review
• Keeping your own voice, and declaring your AI use

You produce: an abstract and methods section for your project.

Jupyter, Git, environments and version-control basics.

Topics covered

• Organising analysis in Jupyter notebooks
• Python environments and dependencies
• Version control basics with Git
• Making your results reproducible by others

You produce: a reproducible project repository.

Analyse a real dataset in a clean, reproducible notebook.

Topics covered

• Choosing a dataset from your own coursework or research
• Cleaning, analysing and visualising the data
• Writing up your insights
• Sharing a clean, reproducible Jupyter notebook

You produce: your capstone notebook.