Chapter 0 of Foundations

Start here

How the course is organised, how to read a page, and how to get a working Python environment in the browser or on your own machine. A short self-check tells you whether to begin at Chapter 1 or take a Python refresher first.

3 episodes35 min of teaching30 min of exercisesabout 1 h 05 min in all

What you will be able to do

Navigate the course: find any episode by its number, use the Overview and Key points cards, and track your own progress.
Recognise every kind of box (note, pitfall, predict, bridge, deep dive, exercise, checkpoint) and know what each one asks of you.
Run Python code either in Google Colab with nothing installed, or in a local environment built from environment.yml or requirements.txt, and confirm it works with scripts/check_env.py.
Judge honestly whether your Python is ready for Chapter 1, and know where to brush up if it is not.

Schedule

Episode	Cumulative time	Questions
0.1How this course works 10 min teaching + 5 min exercises	15 min	How is the course organised, and what does a number like 2.3 mean? What are all the boxes on a page for, and which ones can I skip? How do I find my way around, and what does the site remember?
0.2Setting up Python 20 min teaching + 10 min exercises	45 min	Do I have to install anything to follow the course? If I install locally, which of conda, mamba, uv, pip and venv should I use, and why do the versions matter? How do I know my setup actually works, and what do I do when it does not?
0.3Python self-check 5 min teaching + 15 min exercises	1 h 05 min	Is the Python I already know enough to start Chapter 1? Which habits (indexing, slicing, tracebacks, floats) will trip me up if I do not fix them now?

Before you start

Prerequisite

Nothing, except one introductory programming course in any language and a willingness to type things in. If you have never written a loop or a function, do the self-check first; its closing section lists three free refreshers that will get you there in a week or two.

Suggested paths by background

The course is written for four kinds of graduate. Everyone reads the same episodes, but you will find different parts easy, and the bridge boxes (Chemist’s corner, Biologist’s corner, Maths corner, For programmers) are there to catch you where your background does not. The maths refresher covers vector spaces and matrices, inner products and norms, derivatives and gradients, probability, sample statistics, and logarithms; the right column says which of those to skim before Chapter 2.

Background	What will feel easy	Where to slow down	Skim first in the maths refresher
Informatics / computer science	Python, environments, notebooks, debugging. Chapter 1 will be quick; read it for the NumPy and pandas idioms rather than the syntax.	Chapter 2 onwards, where the data carry meaning. The chemistry and biology bridge boxes are not optional for you: a model is only as good as your grasp of what a solubility or an expression value actually measures.	Derivatives and gradients; probability; mean, variance and distributions.
Chemistry	The scientific questions. You already know why solubility, lipophilicity and molecular size matter, which is half of Chapter 2 and all of Part II.	Chapter 1. Take the full time on arrays, DataFrames and plots, and do every exercise; the code habits you build there carry the whole course.	Vectors, matrices and inner products; logarithms (logS and pKa are both base-10 logarithms, and the course leans on that).
Biology	Experimental design: blinding, replicates and controls are exactly the ideas behind test sets, cross-validation and baselines in Chapter 2.	Chapter 1, as for chemists, and the maths in the deep-dive boxes of Chapter 2. Open them: they are short and they explain why the methods work, not just how.	Probability; mean, variance and distributions; logarithms; then vectors and matrices.
Mathematics	Everything in the deep-dive boxes, and the linear algebra of Chapter 2. You will want the formal view early; the Maths corner boxes give it.	Chapter 1 for the tooling, and the parts of every chapter that are about data rather than theorems: units, missing values, what a dataset does and does not represent.	None needed. Instead read the reproducibility checklist at the end of episode 2.6 before Chapter 2, so that seeds and splits make sense from the start.

Whatever your background, do the three episodes of this chapter in order. Episode 0.1 takes ten minutes, episode 0.2 gives you somewhere to run code, and episode 0.3 tells you whether to start Chapter 1 now or after a short refresher.