GESIS Training Courses

Wiss. Koordination

Kathrin Busch
Tel: +49 221 47694-226

Administrative Koordination

Angelika Ruf
Tel: +49 221 47694-162

Week 1: Fundamentals of Data Analysis with Python

Dr. John McLevey, Jillian Anderson

Datum: 02.03 - 06.03.2020 ics-Datei

Veranstaltungsort: Cologne / Course language: English

Referenteninformationen - Dr. John McLevey

Referenteninformationen - Jillian Anderson


This course is a hands-on introduction to data analysis in Python for social scientists. It is designed primarily for social scientists who have little to no previous experience with Python, and with varying levels of experience with quantitative and computational data analysis. The course covers a variety of foundational topics related to collecting, cleaning, munging, exploring, and visualizing data. It begins with an introduction to programming in Python for social scientists, covering variables, conditional execution, basic data structures, and functions and methods. It then covers collecting digital behavioural data using web scrapers and application programming interfaces (APIs), cleaning structured and unstructured data, and doing simple data visualizations and exploratory analysis. Upon successful completion of the course, students will have a solid foundation for future learning, including network analysis, natural language processing, and various applications of machine learning algorithms.


Participants will find the course useful if they
  • are social scientists interested in doing computational social science but lack experience in using Python or other programming languages (e.g. R) to analyse data,
  • want to learn how to collect, clean, explore, and visualize digital behavioural data.


By the end of the course participants will
  • have foundational knowledge of Python for data analysis,
  • be able to scrape structured and unstructured data from the web and from document collections,
  • be able to collect digital data from Application Programming Interfaces (APIs),
  • organize, clean, and reshape data using Python's “scientific stack”,
  • getting started with visualization and exploratory data analysis.


  • This course is primarily focused on the basics of collecting, cleaning, exploring, and visualizing data with Python. While some experience with other programming languages (e.g. R) is an asset, it is not required or assumed.
  • A basic familiarity with research design, methods, and quantitative data analysis in the social sciences is recommended

Python, Pandas, Exploratory Data Analysis, Data Cleaning, Visualization



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