Introduction to Programming

Course logistics

Prof. Dr. Gerit Wagner

Welcome

Prof. Dr. Gerit Wagner

Academic background

• 2009-2018: Universität Regensburg (Bachelor/Master/Doctoral Student)
• 2014: University of Oulu (ERASMUS)
• 2019-2022: HEC Montréal (Postdoctoral Fellowship)
• 2022-2026: Otto-Friedrich-Universität Bamberg (Assistant Professor)
• Since 2026: Frankfurt School of Finance & Management (Full Professor)

My teaching and research interests

• Programming (on GitHub)
• AI for knowledge work, health, and literature reviews

Things I do outside of work

• Running and working out in the gym
• Hiking, skiing, sailing with friends
• Contribute to OpenStreetMap using the StreetComplete app

About you

1. Where are you from?

2. Which sports do you do?

3. Have you programmed before?

• In which context—for example, at university, at work, or for a personal project?
• Which programming languages or technical tools have you used?

4. What are your expectations for the course?

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

Please use your name tags.

Introduction to Programming

Definitions

programming

noun

The process of writing and testing a sequence of instructions that a computer can follow to perform specific tasks.

ChatGPT

noun

A large language model by OpenAI that can generate, explain, and debug code — often surprisingly accurately.

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

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.

Course logistics

Course structure

Session	Focus	Topics
1	Excel	Basics, formulas, charts, and conditional structures
2	Excel	Lookups and tables
3	Excel	Pivot tables
4	Python/Pandas	Python and pandas basics
5	Python/Pandas	Data access and manipulation
	Exam	Mid-term exam: Excel — 40 points
6	Python/Pandas	Statistics, charts, and grouping
7	Python	Pivoting, prediction, and data types
8	Python	Sequences and conditionals
9	Python	For and while loops
10	Python	Loop control, functions, and dictionaries
11	Python	Pandas automation and web APIs
	Exam	Final exam: Python — 80 points

Course logistics

Workload:

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

Contact:

• E-mail: g.wagner@fs.de

Workshop: Python Setup for macOS Users

Join us for a hands-on workshop on setting up Python on macOS.

Date: September 8, 2026
Time: 18:00 CET

Please scan the QR code below to register for the workshop:

For a successful course

I hope you enjoy the course, learn a lot, and come away with skills that you can actually use.

• I hope you develop a good rhythm early and find a way of learning that works well for you.
• I wish you the perseverance and confidence to keep going when things do not work immediately.
• I hope you support each other, ask questions, and make good use of the tutoring sessions.
• And, of course, I wish you success in the course and in the exams.

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