Microcredential ekomex: Introduction to Python


Crash course on learning Python, from variables to basic data analysis.

What Is This Course About?
This course is designed to provide an introductory overview of using the Python programmatic language for basic programming tasks, as well as for data analysis and visualisation of quantitative (social science) data. For that purpose, our schedule alternates between lecture sessions that present the theoretical and technical background of data analysis and practical sessions that allow participants to directly apply acquired knowledge with code in the Python programming language. This course will enable participants to learn basic programming concepts such as variables and data structures as well as show them how they can use Python to load, preprocess, analyse, and visualise data.

Learning Goals

  • Become familiarised with introductory programming concepts and the way they are used in Python
  • Have general knowledge of the Python Programming language
  • Code simple data analysis scripts using the Python Data Science Stack and Jupyter Notebooks
  • Do Data Exploration and Preprocessing with Pandas
  • Create visualisations with Python for quantitative data analysis

Assignments for the Course
We will have several small in-class exercises to be solved individually or in groups of two lasting 15-20 mins each on all days of the course.

Recommended Readings for the Course

Who is Your Instructor?
Indira Sen is a Postdoc at the Political Science department at the University of Konstanz and her research is about understanding and characterizing the measurement quality of social science constructs like political attitudes and abusive content from digital traces. Her work with NLP and measurement theory. You can reach her at @indiiigosky or https://indiiigo.github.io/.

Bildungszeit (can be claimed by employees in Baden-Württemberg) 
Anforderungen des Bildungszeitgesetzes Baden-Württemberg sind erfüllt
120 EUR / Please note: you will gain access to our learning management system Moodle only after having paid your course fee
ECTS Credits 
Contact for Questions 
22.02.2024 (All day)
23.02.2024 (All day)
2 study days
No prior knowledge is required other than basic knowledge of empirical research, such as what is a case, what is a variable, what is data.