index.qmd

---
title: "Welcome to Programming with Python!"
subtitle: "A course to learn Python for Business"
html: default
---

# Objectives
This module introduces **programming with python**. Python is a modern and powerful programming language that is widely used in industry and academic projects. Students will learn how to find a code-based solution to basic and complex problems. The course is based on many examples that illustrate how to tackle a potentially complex issue as well as how to implement a solution.

# Learning Outcomes
Upon completion of the course, students …

- can implement solutions to complex problems in python
- know basic concepts of programming and algorithms such as loops, functions
- can apply basic data manipulation and visualization
- will be able to read and write code
- will have experience working with python libraries such as NumPy, Pandas, Matplotlib
- will know how to collaborate in a pair to find solutions for problems at hand

Please note that this course is **specifically designed for business students**. That is, it is **not required to have any prior knowledge or experience in programming** to attend this course. The teaching format will make it possible to account for different levels of programming skills such that every student can take the most out of the course.

# Course Structure
The course is based on a hands-on approach with three elements:

1. **Lectures**: In the lectures, we will introduce concepts and illustrate them in reproducible examples.
2. **Hands-on Tutorials**: Students will practically apply the concepts of the lecture in hands-on examples.
3. **Checkpoints**: Five short in-class checkpoints let you show what you have learned. They run in the browser, are solved individually, and are graded automatically.

# Course Blocks
The core content of the course is organized in three blocks:

1. **Part I**: Introduction to Programming with Python
2. **Part II**: Data Science with Python
3. **Part III**: Programming Projects

You can find more information on the course blocks and the corresponding lectures in the [syllabus](general/syllabus.qmd).

# How to see the slides
- This course is based on [Quarto](https://quarto.org/)
- It uses [revealjs](https://revealjs.com/) to render the slides
- You find the slides for each lecture in the corresponding lecture
- To see the slides, click on `RevealJS` in the top right corner

# Passing the Course

- 75% attendance is required to pass the course
- Five in-class checkpoints, worth 12 points each (60 points total)
- One final project with a presentation, worth 40 points
- You need 60 of 100 points to pass
- Your project is done in pairs (solo works if the numbers don't come out even); each pair submits one GitHub repository

## AI Policy

The course uses a phased AI policy that mirrors how the skills build on each other:

- **Part I (Sessions I–V): AI-free.** You are building foundations. The course chatbot on this website is the sanctioned helper: it gives hints, not solutions. Checkpoints 1–3 are taken without AI.
- **Part II (Sessions VI–IX): AI allowed and taught.** We bring AI in deliberately: how to prompt with context and constraints, how to verify its output, and how to notice when it confidently makes things up. Checkpoints 4–5 explicitly allow AI tools.
- **Part III (Sessions X–XIII): AI encouraged.** Use what makes you productive on your project, and disclose what you used in your repository's README.

Whatever the phase: understand every line you submit. See the [AI Tools guide](general/ai-tools.qmd) for free options and setup.

# Questions
If you have any questions regarding the course, please contact me under [vlcek@beyondsimulations.com](mailto:vlcek@beyondsimulations.com?subject=ProgrammingPythonKLU26).

# Contributors

Thanks to [Phillip Bach](https://github.com/PhilippBach) whose previous course laid the foundation for this course.