Course Information

What this course is about

Data science is mostly about getting, cleaning, exploring, and explaining data—clearly and reproducibly. In this course you’ll learn to do exactly that with modern tools. We’ll write code, make plots, and (most importantly) make our work repeatable so that someone else—and future‑you—can run it and get the same results.

You can use Python or R throughout the course. Pick the one you’re most comfortable with (or want to learn); the ideas are the same.

How the course runs

This is a self‑study‑first course. Each week you read/watch the materials, try things, and work on a small assignment. Instead of traditional lectures, we meet once a week for a live demo of last week’s material—think short walk‑through

• tips + Q&A—followed by drop‑in help time. We also host Thursday Zoom hours for questions.

• Exact days/times/links are on the relevant pages: Lectures • Homework • Project • Exam

What you’ll be able to do

By the end, you will be able to:

• Use Git/GitHub for version control, collaborate with others, and review code.
• Build reproducible analyses in Python or R (clear README, tidy notebooks/scripts, and a workflow others can run).
• Retrieve, transform, explore, and visualize data with confidence.
• Communicate results in a way that’s honest, clear, and useful.

How you’re assessed (short version)

You’ll practice every week, do an individual project, and finish with an exam.

• Home assignments: Weekly. Focus on the data‑analysis pipeline: load → transform → explore → visualize → report. Expect roughly half‑time study each week. Details: Homework.
• Project: Pick a public/open dataset and tell a clear, reproducible data story. Details, milestones, and rubric: Project.
• Exam: You’ll apply course methods to a given problem. Format and scheduling: Exam.

Passing rules, exact deadlines, late/resubmission policies, and feedback timelines live on the relevant pages (and are listed on the relevant pages and on Moodle).

The final course grade combines all three components. See Final Grade Explanation for details.

Getting help

• Live demo + drop‑in + Zoom: weekly—see Lectures for times and rooms.
• TAs & contacts: see Moodle for the current TA list and how to reach us.

A note on collaboration & AI tools

We encourage discussion and learning together, but there are clear rules about what must be your own work.

Any AI assistance (e.g., ChatGPT/Copilot) must be cited clearly. Flagrant copy/paste or uncredited AI will be considered plagiarism.

Instructor

Taariq Nazar — profile For TA contact info, see Moodle page.

Acknowledgements

This course builds on ideas and materials by Martin Sköld, Erik Thorsén, Michael Höhle, and Felix Günther. Thank you!