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Introduction to Programming in Python (RSS00.02) -Closed

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



    This course is closed, registration is no longer possible. 

    This Python course is designed especially for social scientists who have limited or no programming experience. Therefore, the course focuses on hands-on exercises and practical tips to help you start your journey in the world of Python. 

    The course programme consists of an introduction to Python and Jupyter Notebook, practice with Python, learn how to extract data from the internet via webscraping, learn how to use APIs and an introduction to the basic data analysis toolkit of Python.

    To keep classes interactive, each class requires preparation – such as watching videos that explain theory and methods, going through code, and tiny exercises.   

    How will the course work online? 

    Introductory pre-recorded videos and required readings (mainly documentation of the libraries) will help you prepare for classes. The first 2 hours of each class will focus on introducing new materials, then after the lunch break you will code, either alone or in groups, with live support from the instructor and Teaching Assistant.


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    Cannot join us this year? 

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    Starting date

    10 June 2024, 9 am
    Educational method
    Main Language
    10 June 2024, 9 am - 14 June 2024, 5 am
    Orsolya Vasarhelyi
    Unique code


    Type of education
    Entry requirements
    Study load (ECTS)
    Radboud Summer School
    • 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 conditions. 
    • Day 2: Learn Python the fun way 
      Practice, practice and practice! Bunch of fun coding games to master the syntax and learn to collaborate in teams. 
    • Day 3: 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. 
    • Day 4: Data collection II – API 
      Most social media sites such as Facebook and Twitter, and Wikipedia, allow scientists to collect publicly available data from their services through Application Programming Interfaces: APIs. Learn how to use APIs, (understanding the documentation, parsing json files), and collect and save data different APIs. 
    • Day 5: Data analysis with pandas and introduction to data visualisation 
      We introduce the basic data analysis toolkit of Python (Pandas, Matplotlib, Seaborn). You will work in groups to analyse a pre-defined database, then present your findings to the class. 

    Online course

    This is an online course, which means that unfortunately the social activities of the Radboud Summer School do not apply to this course. In addition, the program will also differ from the weekly schedule published on the website. 

    Two weeks before the course starts you will be added to our digital learning environment Brightspace, where the new times will be communicated.

    Orsolya Vasarhelyi

    Orsolya Vasarhelyi

    Orsolya Vasarhelyi is assistant professor at the Center for Collective Learning, and at the Institute of Data Analytics and Information Systems at Corvinus University in Budapest, Hungary. Her research lies at the intersection of computational social science, gender studies, and human-machine interaction. Using interdisciplinary approaches from network and data science, she investigates how networks induce inequalities.

    This course is closed, registration is no longer possible. 



    • Regular: €699 (application deadline 1st of May)
    • Student & PhD's: €466 (application deadline 1st of May)

    Discounts and Scholarships


    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: None

    Admission documents: None