Introduction to Programming

Course logistics

Prof. Dr. Gerit Wagner

2026-03-23

TODO

• Get syllabus and integrate learning objectives
• Start with a strong focus on programming (I love programming/Github stats, first student assistant job, …, student coauthors, but also: teaching it in an accessible way; move research interests to the end: theses)

Welcome

Prof. Dr. Gerit Wagner

Academic background

• Universität Regensburg: Doctoral Student
• HEC Montréal: Postdoctoral Fellowship
• Otto-Friedrich-Universität Bamberg: Assistant Professor
• Frankfurt School of Finance & Management: Full Professor

Research interests

• Open, agentic, and boundary-spanning work
• AI-supported knowledge synthesis

Teaching interests

• Programming and software engineering
• Analytics and big data
• IT security
• Digital knowledge-intensive and platform-based work
• Literature review methods

About you

Please take a moment to think about the following questions. For each question, I’ll invite a few of you to briefly share your thoughts.

1. Have you programmed before?

• In which context did you use programming—for example, at university, at work, or for a personal project?
• Or is this your first programming course?

2. Which programming languages or technical tools have you used?

• Was there something you found particularly interesting or useful?
• Or something that felt especially difficult or confusing?

3. What are your expectations for the course?

• What would you like to be able to build, automate, or analyze with Python?
• What would make this course worth it for you?

Introduction to Programming

Programming in the curriculum

What exactly is programming?

Programming is the process of translating a problem into precise instructions that a computer can execute.

A program connects:

Problem → Representation → Instructions → Execution → Result

Programming therefore involves more than writing code:

• understanding the problem,
• breaking it into manageable parts,
• representing data appropriately,
• defining precise rules and procedures,
• testing whether the solution works,
• finding and correcting errors.

Discussion: What is programming—and why can’t ChatGPT just do it?

Take 2–3 minutes to discuss with the person next to you:

If ChatGPT can generate Python code, why should you still learn programming?

Consider:

• Who defines the problem?
• How do you know whether the generated solution is correct?
• What happens when requirements are incomplete or ambiguous?
• Who finds errors that produce plausible-looking results?
• How do you adapt a solution when the problem changes?

Reflection

AI can generate code, but programming also requires problem formulation, decomposition, evaluation, debugging, and responsibility for the result.

The goal of this course is therefore not simply to memorize syntax. It is to learn how to think precisely about problems and computational solutions.

Why programming (and this course) matter

Business analysis — From spreadsheet to model

• Structure business problems explicitly
• Transform raw data into calculations and decisions
• Build transparent and reproducible analyses

Data analysis — Automating repetitive work

• Process larger amounts of data
• Repeat the same analysis consistently
• Combine data preparation, analysis, and visualization

Problem solving — From error to understanding

• Translate requirements into precise steps
• Test assumptions systematically
• Find, understand, and correct errors

What you should be able to do

By the end of the course, you should be able to:

• explain how computer programs work,
• describe the basic principles of spreadsheet applications,
• explain fundamental Python data types and control structures,
• use a development environment effectively,

and, more importantly:

• translate business and numerical problems into computational solutions,
• select suitable data structures,
• develop simple Excel and Python applications,
• find and fix errors,
• implement solutions with attention to quality.

What this course does not primarily cover

• Advanced software engineering

  • large application architectures,
  • distributed systems,
  • production deployment,
  • DevOps and MLOps.

• Advanced computer science

  • algorithmic complexity theory,
  • compiler construction,
  • operating systems.

• Advanced machine learning

  • model training and optimization,
  • deep learning,
  • autonomous agents.

• Prompting as a substitute for understanding

  • copying generated code without evaluation,
  • treating plausible output as evidence of correctness.

The focus is on learning how to formulate problems, build solutions, inspect results, and debug systematically.

Course logistics

Course logistics

Workload:

• 150 hours total
• 44 academic teaching hours
• remaining workload: preparation, exercises, revision, and exam preparation

Course structure:

• Q1: Excel
• Q2: Python
• practical exercises in class and at home

Assessment:

• Excel: 40 minutes, 40 points, mid-term examination period
• Python: 80 minutes, 80 points, examination period

Contact:

• E-mail: g.wagner@fs.de
• Office hours: booking page

Individual circumstances:

If you have family responsibilities, religious holidays, health-related matters, or other individual circumstances that may affect your participation or performance, please reach out early.

We will work together to find a fair and workable solution.

How we will learn programming

Programming is a practice, not a spectator sport.

During the course, you will repeatedly:

Read → Predict → Run → Inspect → Modify → Break → Debug → Explain

You should expect to:

• work through examples during class,
• solve practical exercises,
• compare alternative solutions,
• explain what code does before running it,
• debug code that does not work,
• gradually solve less structured problems.

A final question for today

When you ask ChatGPT to write a program, who is actually doing the programming?

Think about the different roles involved:

Problem owner → Problem formulation → Solution design → Code generation → Testing → Evaluation → Responsibility

Which of these can be delegated?

Which cannot?

And which should not?

References