Lecture XIII - Presentations and Discussion

Programming with Python

Author
Affiliation

Dr. Tobias Vlćek

Kühne Logistics University Hamburg - Fall 2026

EP 13: Demo Day

The investor is in the room

Back in Session VI she stood at a whiteboard and asked why everything here was built from scratch. Today the answer is on your screens: built by you, with the standard library, pandas, plots, git, and an agent you learned to supervise.

The rules: 7 minutes presenting, 3 minutes of questions, order drawn at random.

While you watch

  • Note one thing you would have done differently, and one thing you are taking for your own next project
  • Questions are part of the format: specific, kind, useful

Your Future with AI

AI and your first job

  • Large experiments with thousands of developers: +26% tasks completed with AI, and juniors gained the most
  • But experienced developers on their own codebases went 19% slower while feeling 20% faster
  • And a learning study: students coding with AI scored 50% on a mastery quiz vs 67% without, worst gap on debugging
  • The same study family found the fix: guarded use, hints instead of finished solutions, erased the harm. Part I was exactly that

What stays valuable

  • The verification boundary: usable delegation ends where your ability to check ends. Growing that boundary outward is your career
  • Comprehension debt: code that works today but nobody understands tomorrow. The interest comes due when a reviewer asks “why does this work?”
  • Your edge, deliberately kept: explain every line before you accept it, and code without AI sometimes

Discussion: your future with AI

Three questions, five minutes, show of hands:

  • Which task from the past weeks would you never delegate to an AI?
  • What will an entry-level logistics analyst do in 2030, when AI drafts the first version of everything?
  • If AI writes most junior code, where do the senior people come from?

Send-off

Keep programming

The skill fades if it sits idle. A few ways to keep it alive:

  • Use Python in your thesis: data cleaning, analysis, plots you can defend
  • Find a way to apply it at work: the repetitive task nobody wants to do by hand
  • Advent of Code: free, ad-free programming puzzles, one a day from December 1st. A genuinely fun way to keep the muscle warm.

Thank you

  • We covered the basics of Python: from a first print to a real project on your own machine
  • You debugged, you verified AI, you shipped real numbers
  • I hope you enjoyed it, and I hope it’s useful long after the grade
  • If you have questions or feedback, please tell me

All the best for your studies and your career. Now go build the thing.

Literature

Books to start with

  • Downey, A. B. (2024). Think Python: How to think like a computer scientist (Third edition). O’Reilly. Link to free online version
  • Elter, S. (2021). Schrödinger programmiert Python: Das etwas andere Fachbuch (1. Auflage). Rheinwerk Verlag.

For more, see the literature list of this course.