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Python Programming for Social Scientists

Course Dates and Times

Monday 18 ꟷ Friday 22 July 2022
2 hours of live teaching per day
10:30 ꟷ 12:45 CEST

Rebeka O. Szabo

Corvinus University of Budapest

This course provides a highly interactive online teaching and learning environment, using state-of-the-art online pedagogical tools. It is designed for a demanding audience and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the needs of each individual.

Purpose of the course

Python is one of the most popular programming languages of data science, used in natural language processing, machine learning, and artificial intelligence. This five-day Python programming course is designed for social scientists who would like to learn how to conduct data collection and complex data analysis with Python. 

The course focuses on hands-on exercises and practical tips to help you start your journey in the world of Python. This course covers a wide variety of topics in a short period of time, so you will complete after-class (homework) assignments from Monday to Thursday.

ECTS Credits

3 credits Engage fully with class activities
4 credits Complete a complex post-class assignment

Instructor Bio

Rebeka O. Szabó is a sociologist-network scientist.

She is a junior researcher at the Laboratory for Networks, Innovation, and Technology at Corvinus University of Budapest.

Rebeka earned her PhD in network science at Central European University and her Master's degree in sociology at Universiteit Van Amsterdam. She was a visiting research fellow at Kellogg School of Management and The Northwestern Institute on Complex Systems of Northwestern University in 2020.

Her main scientific interests are teams, group dynamics, social networks, applied social psychology, and organisations intertwined with topics of cooperation and social inequality.

Twitter  @RebekaOSzabo

Before the course

There is around three hours' preparatory work for Day 1. This includes:

  • Creating a Google drive folder and sharing it with the Instructors
  • Joining the Slack group
  • Downloading Zoom
  • Watching videos
  • Downloading the files for the Day 1 class.
Day 1

Introduction to Python and Jupyter Notebook
Learn how to operate Jupyter Notebooks, through Google Colab. We also cover different data types in Python, loops, and conditional statements.
Homework: Set of programming games

Day 2

Data collection I – Web scraping
Python is a popular language to extract data from the internet. Learn how to extract data from semi-structured websites and save the results into .xlsx and .csv files.
Homework: Scraper for a pre-defined website

Day 3

Data analysis I – Intro to data cleaning, analysis and nested data structures
Data cleaning is one of the most challenging parts of the work of a data scientist. Learn how to extract relevant information from messy data and create data structures that are efficient to use.
Homework: Write functions – combine loops and conditions

Day 4

Data analysis II – Data analysis with Pandas and data visualisation
A picture is worth a thousand words. Besides introducing Python's most popular data analysis toolkits (Pandas, Matpotlib, Seaborn), you will also learn how to convey the findings of your analysis effectively by creating appealing and scientifically valid visualisations. You will work in groups to analyse a pre-defined database, then present your findings to the class.
Homework: Exploratory data analysis with visualisations on a pre-defined data set

Day 5

Data analysis II – Statistical modelling
How to conduct statistical modelling in Python. We will focus on the two most popular libraries:

  • Statsmodels Great for regressions and statistical tests
  • Scipy Performs machine learning

We'll also learn about PCA and freely available data sets you might choose for your post-class assignment.

How the course will work online

Introductory pre-recorded videos and required readings (mainly documentation of the libraries) will help you prepare for classes. The first half of each class will focus on introducing new materials, then you will code, either alone or in groups, with live support from the Instructor and Teaching Assistant. 

Homework assignments on Days 1–4 will deepen your knowledge of each topic. Your homework will be checked by the Instructor and TA, and you can book one-to-one meetings with them to discuss it.

Basic statistical knowledge, no programming experience needed.

Before the course

There are around three hours of preparation for Day 1. This includes:

  • Creating a Google drive folder and sharing it with the Instructor(s)
  • Joining the Slack group
  • Downloading Zoom
  • Watching videos
  • Downloading the files for the Day 1 class.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.