Introduction to Python (with data analysis applications)¶

Overview¶

This is a project-based, in-person beginner Python course built around collaboration and real-world practice. You’ll grow by actively working with others while developing a complete data-processing application from start to finish, relying on practical tools and workflows commonly used in data science.

We take the “working with other people” part seriously. Your code will live in a GitHub repository, and you’ll participate in a structured process of giving and receiving feedback — similar to a professional development environment. Throughout the course, you’ll engage in interactive code reviews and continuous code improvement. Together, we’ll build a common project, creating space to experiment, make mistakes and learn from them.

Target audience¶

People of all ages are welcome (16 and above). We do not assume prior experience with python, data analysis or math. It would help if you have used some other programming language before (even if you don't remember the details), but this is not a prerequisite. However, we assume that you have your own computer that you can bring and work with¹ — learning to program is not a spectator sport, you have to jump in and get your hands dirty.

What will I learn?¶

You will learn:

• To use Git (and GitHub) — this would lead to a major change in the way you work!
• To formulate and solve problems using Python:
  • basic syntax, expressions, and statements
  • how and when to use: lists, dictionaries, sets, tuples, ...
  • string processing and regular expressions
  • control flow: loops, conditionals
  • functions, error handling, working with files
  • object-oriented programming.
• To use the python ecosystem as well as to create and publish your own projects.
• To structure, test and document your code.
• To use standard tools in data science
  • Numpy and Pandas for data processing and analysis
  • scikit-learn for building and using machine-learning models — we will discuss regression models and least-squares.
• To visualize your data in a Jupyter notebook using Matplotlib and other libraries.
• To create a static website on which to describe your results.

In the end, you will have a solid base to build on (please read this famous blog post by Peter Norvig to set expectations).

Project description¶

This is a month-long course centered around building a complete data-processing application from scratch. The core project is split into several parts and focuses on developing a system that:

• Accepts as input a simplified version of a double-entry bookkeeping data format (used in ledger cli). This is a concrete example of a structured, non-standard format (something we often need to deal with in practice).
• Parses the input data using Python (giving you hands-on experience with string processing and file handling).
• Stores the parsed data in carefully designed Python data structures (introducing concepts of code organization and data abstraction).
• Transforms, analyzes, and visualizes the processed data (providing practical exposure to real-world data science techniques).
• Builds simple models and uncovers insights from the data (applying statistical and analytical reasoning).
• Generates reports based on the analysis (learn how to communicate results clearly).
• Optional challenges are available for those interested in more advanced mathematical applications. You’ll also get help to apply what you learn to your own side projects.

Course format¶

There are three ways to participate in the course. You can choose the level of involvement that fits your schedule — though more participation will naturally lead to greater benefit.

GitHub Discussions / Issues¶

• Ask questions, post issues and share progress
• Get feedback and participate in technical discussions.

This is how developers collaborate and advance their projects. This option allows you to participate remotely and asynchronously.

Live coding sessions (on-site)¶

We will hold in-person live coding sessions during the following time slots²:

• Mondays: 18:00 — 20:00
• Wednesdays: 14:00 — 18:00
• Saturdays: 09:30 — 17:30 (with an hour and a half of lunch break).

These sessions are strongly encouraged, as they provide hands-on guidance, real-time problem solving, and direct interaction. Regular participation is important and will significantly enhance your learning experience. However, you are not required to attend for the entire duration of each session — you manage your own time. The idea is simple:

Come, work on your project, ask questions, and stay as long as you need — especially until your questions are answered or you feel ready to move forward (active participation matters more than the number of hours you stay).

Dedicated online discussions¶

Whenever needed, we can organize dedicated discussion sessions via e.g., Google Meet to address pressing questions.

In short: you’ll have every opportunity, resource, and bit of guidance you need to challenge yourself and expand your skills! This is around 50 hours of live-coding plus the option to have remote discussions (whenever necessary).

Can I really follow this course — I haven't done data science before?¶

Don't Panic! This course is designed for beginners. While some of the material may feel challenging at times, that’s completely normal and part of the process. By learning Python in the context of data science, you’ll get a sense of just how useful a language it is (instead of simply focusing on abstract language syntax — something you can easily find online).