Introduction to Programming with Python
Day 1

David Garcia, University of Konstanz

About me


  • Professor for Social and Behavioral Data Science
  • Faculty member of the Complexity Science Hub Vienna
  • External faculty at the Barcelona Super Computing Center
  • Privatdozent at ETH Zurich
  • Assistants in WS 2025/26: Anastasia Siebers and Niklas Bacher

Course aims

After this course, you should:

  • Have a solid foundation in Python programming.

  • Understand fundamental programming concepts such as variables, control flow, basic data structures, and functions,

  • Know how to load, manipulate, and visualize data in Python

  • Be able to apply these skills to basic data analysis and programming in other courses and to start developing more advanced, domain-specific Python skills.

Target audience

This course is designed for first-semester students of Social and Economic Data Science

  • Who has ever written a simple script?
  • Who has learned programming before?
  • In which languages?
  • The target audience is students with nearly zero programming experience. There will be extra ungraded exercises for those of you with more experience.

Teaching approach

  • Mornings: Interactive lectures that cover theoretical and technical aspects of programming with Python
    • Mostly based on going together through notebooks with concepts and exercises
    • It is essential that you have the notebook in front of you and that you try the coding exercises
    • The best time to ask questions about understanding
  • Afternoons: Practical tutorials where you apply the acquired knowledge in assignments
    • Self-work with support from tutors
    • The best time to get technical support and to ask questions about assignments

Course overview

  • Day 1:
    • Variables, strings and numbers
    • Lists and tuples basics. Looping over lists
  • Day 2:
    • Tuples and lists advanced
    • Conditional commands
  • Day 3:
    • Dictionaries
    • Functions
  • Day 4:
    • Exceptions
    • Files
    • Data exploration with pandas
  • Day 5:
    • Data visualization
    • Final exam

Resources and help

To pass the course, you must:

  1. Pass each of the five daily assignments (at least 50% points in each)
  • The deadline is 23:59 in each of the days of the course
  • Submissions are done through Ilias. Familiarize yourself with Ilias as soon as possible, talk to the tutors in the afternoon if you need help
  • There are advanced, ungraded assignments for you who have more experience. Don’t submit those, we won’t grade them
  1. Pass the final written exam (at least 50% points)
  • 90-minutes written exam on Friday before lunch
  • Not writing scripts on pen an paper, code input will be very short
  • Designed to check experience with Python and conceptual knowledge
  • About programming! Not about memorizing stuff from slides or notebooks. Quizzes from day 2 will provide example questions

Registering for “exam”

  • This course is only pass/fail, there is no numeric grade

  • We will inform you of whether you pass fail in the two weeks after the course

  • Then, between December 1 and January 15th, you have to “REGISTER FOR THE EXAM” in Zeus

    • You need to do this to get the course credited at the end of the semester
    • Make a note in your calendar because you can forget
    • You will have to do this for other courses each semester, including seminars, tutorials, etc

Warning about vibe coding (e.g. ChatGPT)

Vibe coding: Using chatbots (ChatGPT, Claude, Gemini) to write the code of a software project and using the software without understanding the code

Vibe coding is OK for hobbyist but a professional data scientist is responsible of the code they run and deliver.

It’s OK to discuss your work with a chatbot but not to copy-paste code from a chatbot or anywhere else (e.g. Stackoverflow, another student)

Coding plugins might use AI for autocomplete but you must always understand your code

Anaconda and jupyter

Are you set up? – Break for setup support, check instructions in: setup/Anaconda.html