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Python Programming for Social Scientists

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Monday 6 – Friday 10 February 2023
Minimum 2 hours of live teaching per day
10:00 - 12:15 CET

Orsolya Vasarhelyi

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 for social scientists who want to learn how to conduct data collection and complex data analysis with Python. 

Hands-on exercises and practical tips will help you start your journey in the world of Python. The 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

Orsolya Vasarhelyi is an 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 focuses on the gender differences in career development in project-based environments.

She is a Python enthusiast!

Key topics covered
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 a data scientist's work. 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.