2025-04-23 –, Ferrum
In this tutorial, you will speed-date with board and card games that can be used to teach Data Science. You will play one game for 15 minutes, reflect on the Data Science concepts it involves, and then rotate to the next table.
As a result, you will experience multiple ideas that you can use to make complex ideas more understandable and enjoyable. We would like to demonstrate how gamification can not only used to produce short puzzles and quizzes, but also as a tool to reason complex problem-solving strategies.
We will bring a set of carefully selected games that have been proven effective in teaching statistics, programming, machine learning and other Data Science skills. We also believe that it is probably fun to participate in this tutorial.
Games encourage people to put their brains to work in a focused, constructive and peaceful way. This makes games a fantastic tool in the classroom. Many board games contain sophisticated algorithms and statistical models right under the surface. Therefore, Data Science education can be boosted by playing carefully selected games.
We have applied popular board and card games such as Memory, Wizard, Machi Koro, Pandemic and Sky Team (the 2024 Game of the Year in Germany) to teach Data Science concepts in our courses. Learners would first play a game, discuss the mechanisms and only after that get exposed to the theory. Finally, they would move to practical applications using computers.
This game-driven approach provides learners with an intrinsic motivation to solve a real practical problem (succeeding at the game).
Analyzing a game makes it easier to grasp the core mechanism or algorithmic model and ask qualified questions about the details later.
It also makes sure learners will want to come back for the next class. We have documented practical lessons and made them available under a CC license on https://www.academis.eu/probably_fun/ .
In this tutorial, you will speed-date with several short games that can be used to teach Data Science concepts and skills. You will play one game for 15 minutes, reflect on the Data Science concepts it involves, and then rotate to the next table.
This way, you will experience multiple ideas you can use to make complex methods and ideas more accessible. Also, the tutorial is probably fun to participate in.
The tutorial will be executed according to the following pseudocode (or lesson plan):
- The presenters give a short introduction on why games matter (5 min)
- The presenters group participants into teams of up to 6 people.
- Each team is assigned to a game table with a game and a cheat sheet with instructions. The presenters facilitate with understanding rules and to remove other obstacles.
- The teams play the game for up to 15 minutes.
- The teams discuss 1-3 prepared reflection questions to make the transfer from the game to the data science concepts.
- Each team moves to the next table.
- Repeat for 3-4 rounds.
- Everybody gets together for a joint Q & A
- A QR-Code links to material with games that help learning Data Science and lesson plans
None
Expected audience expertise: Python:None
Public link to supporting material, e.g. videos, Github, etc.:
Data Nerd & Python Pydata community lover. AI education specialist with five years of experience shaping data science and AI educational offers. Currently leading the AI Academy at the appliedAI Institute for Europe.
Kristian is a freelance Python trainer who wrote his first lines of Python in the year 11111001111. After a career writing software for life science research, he has been teaching Python, Data Analysis and Machine Learning throughout Europe since 2011. More recently, he has built data pipelines for the real estate and medical sector.
Kristian has translated 5 Python books and written 2 more himself, in addition to numerous teaching guides. Kristian has collected 364 stars on Advent of Code. His knowledge about async is, unfortunately, miserable. His favorite Python module is 're'. Kristian believes everybody can learn programming.
You can find Kristians teaching materials on https://www.academis.eu