UNIL — Université de LausanneHEC Lausanne Autumn semester 2026

Data Science and Advanced Programming · Resources

Resources

A short list, on purpose. Each item says when to reach for it.

Office hours

When
Fridays, from 13:00 — by appointment only Email us with what you want to discuss and we will give you a slot. Turning up without one does not work: we are often not there, and someone else may already have the time.
Come with
A specific question, and what you already tried "I expected this line to print the total and it printed nothing, and here is what I checked" is a question we can answer in five minutes. "It doesn't work" is not.
Good use of the time
Ideas you could not get from the lecture or the workshop A concept that will not click. An error message you have read and still cannot interpret. How to approach a piece of work. What the exam expects.
Not what this is for
Technical support, or having your code written We do not fix laptops, install software, or debug line by line on your behalf, and we do not repeat a session you missed. The workspace exists so that nobody needs local setup; the materials for a missed session are on this site.

Interactive — best first stop

Visualize
Python Tutor Paste code, watch variables and function calls change step by step. Use it every week — it shows exactly what the lectures describe.
Practice
futurecoder Free, in the browser, starts from absolute zero. The catch-up path if sessions 2–3 move too fast for you.

Books — free online

Practical
Automate the Boring Stuff with Python — Sweigart Files, folders, small useful scripts — closest in spirit to this course.
Careful
Think Python, 3rd edition — Downey A classic, deeper than the course requires. For when you want more.
Part 2
Python for Data Analysis, 3rd edition — McKinney The pandas book, by pandas' author. The reference shelf for the data-science half.

Official documentation — for checking, not learning

Python
The Python Tutorial When your AI assistant claims a function behaves some way, this is the ground truth.
pandas
pandas Getting Started Same role, for Part 2.

AI assistants — the course ground rules

Which tool
Any free chatbot works for the workshops The course requires no specific product and provides none.
Privacy
Never paste people No names, grades, or personal data into any assistant. Course data is synthetic on purpose.
Responsibility
You are responsible for what you keep The model wrote it; you committed it; it is yours now.
Exams
Paper, proctored, no devices The exams test what stays yours: reading code, tracing it, and judging output.

Getting help

In session
Neighbor first, then a hand up Most blockers are thirty-second fixes your neighbor just solved.
Between sessions
Course forum on Moodle Link published at semester start. Public questions get the fastest answers — someone else has your problem too.
Personal matters
anna.smirnova@unil.ch Anna Smirnova, teaching assistant. For anything that shouldn't be public — not for debugging.