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Programming with Python

Semester 3, 2026 · (pass/fail)

Form of Examination

Pass/fail. To pass, students need at least 60 points, attendance of at least 75% of sessions, and a final group presentation.

How You Pass

This course is pass/fail; there are no grades. To pass, you need at least 60 points and attendance of at least 75% of sessions.

Points come from two things: the five checkpoints (12 points each, 60 in total) and the final group project together with its presentation (40 points). That makes 100 points, of which 60 are needed to pass.

The checkpoints

Five exercises, mostly done in class during the term. In each one, you write and run your own code. The tasks vary: writing a function, working out what a piece of code prints, fixing a bug, or answering multiple-choice questions. You work individually.

In Part I the checkpoints are AI-free; in Part II you may use AI (see the AI policy below). The dates are announced at the start of the course.

The final project

In the last part of the course you build a project of your own choosing, in groups of up to 2, and present it in the final session. A list of business-related problems to choose from is provided, but I encourage you to come up with your own idea.

Working with AI

This course follows KLU's general policy on AI tools, linked below, and sets some additonal rules on top of it. The idea behind those rules is straightforward: you need to be able to read and write code yourself before you rely on a tool that writes it for you. The two halves of the course therefore treat AI differently.

Part I (foundations): no AI. While you are learning syntax, control flow, functions, and data structures, AI assistants are not allowed in the checkpoints, and the provided course chatbot gives hints rather than solutions. Using AI in an AI-free checkpoint counts as unauthorised aid under KLU's examination rules.

Parts II and III: AI allowed, and taught. Once the basics are in place, we use AI openly and spend time on how to work with it: how to prompt, how to check and debug what it produces, and how to spot invented functions and APIs. Whenever you use AI in submitted work, including the project, say so and note which tools you used and what for. Undisclosed use is treated as academic misconduct.

Lecturer

Dr. Tobias Vlćek Email: vlcek@beyondsimulations.com

Module Dates

You can find the lecture dates in your myKLU calendar.

Module Objectives

This module introduces programming with Python, a language used widely in industry and research. By the end of the course, students will be able to:

  • implement solutions to basic and complex problems in Python;
  • apply core programming and algorithmic concepts (loops, functions, classes);
  • read and trace code, not only write it (this is assessed directly);
  • apply basic techniques of data manipulation and visualization;
  • work with core Python libraries (NumPy, Pandas, Matplotlib);
  • use AI coding tools with judgement;
  • collaborate in a team to solve a problem.

Note: This course is for business students and assumes no prior programming experience. The format works for a mix of experience levels, so everyone has something to take from it.

Programming Environment

This course uses Python 3 with marimo notebooks that run in the browser, so there is nothing to install before the first session. For the project phase, we set up a local environment and an AI-assisted editor together.

What you need: bring your laptop (not a tablet!) to every session.

Module Structure

The course has three parts.

Part I, Introduction to Programming with Python (AI-free). The basics: syntax, variables and data types; conditionals and loops; functions, scope and classes; lists, tuples, sets and dictionaries with basic input and output; and error handling and debugging.

Part II, Data Science with Python (AI allowed). Modules and packages, numerical computing with NumPy, data handling with Pandas, and plotting with Matplotlib, together with a proper introduction to using AI tools well.

Part III, Programming Project (AI allowed). Students apply what they have learned in a group project and present it in the final session.

The five checkpoints fall in Parts I and II; the dates are announced at the start of the course.

Lecture Materials

Course website and slides: https://beyondsimulations.github.io/Introduction-to-Python/

Required Readings

Literature Session / Date of use M / R Pre-reading
Zingaro, D. (2024). Algorithmic Thinking, 2nd edition: Unlock Your Programming Potential. No Starch Press. [ISBN to confirm] Entire lecture M No
Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist, 3rd edition. O'Reilly. [ISBN to confirm] Entire lecture R No

Additional Readings

  • Advent of Code — daily programming challenges in December; a playful way to practise.
  • Codewars — coding challenges with community solutions to learn from.
  • Tiny Python Projects — small projects to build programming skills.

This course follows KLU's commitment to Inclusion, Diversity & Equality. If you need any accommodations, please contact me.