GESIS Training Courses
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Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Noemi Hartung

Introduction to Computational Social Science with Python

About
Location:
Online via Zoom
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
Keywords
 
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Lecturer(s): John McLevey

About the lecturer - John McLevey

Course description

The Digital Revolution has produced unprecedented amounts of data that are relevant for researchers in the social sciences, from online surveys to social media user data, travel and access data, and digital or digitized text data. How can these masses of raw data be turned into understanding, insight, and knowledge? The goal of this course is to introduce you to Computational Social Science with Python, a powerful programming language that offers a wide variety of tools, used by journalists, data scientists and researchers alike. Unlike many introductions to programming, e.g., in computer science, the focus of this course is on how to explore, obtain, wrangle, visualize, model, and communicate data to address challenges in social science. The course emphasizes the theoretical and ethical aspects of CSS while covering topics such as web scraping (obtaining data from the internet), data cleaning and visualization, computational text analysis, machine learning, network analysis, and agent-based modeling. The course will be held as a blended learning workshop with video lectures focused on theoretical background and demonstrations accompanied by live online sessions where students can ask questions and work through projects together.
 
Organizational Structure of the Course
The course will take place in a blended learning format. That means that you will need to (1.) read the literature listed under each session (if any); (2.) watch the video lecture; (3.) finish the exercises before each live group session. This means that participants will be on roughly the same level of knowledge during the live sessions and we will be able to focus on open discussion, the answering of questions and small group exercises.


Target group

You will find the course useful if:
  • you have taken, e.g., a statistics course, know a little bit of Python, and now want to explore computational methods, data science or one of the approaches listed above.


Learning objectives

By the end of the course, you will:
  • be able to define what constitutes the field of computational social science;
  • have a high-level overview of the approaches utilized in computational social science, including advantages and shortcomings;
  • have a basic knowledge and hands-on experience of how to apply the approaches and what tools are considered state-of-the-art;
  • be equipped to deepen their knowledge on the theory and practice of computational social science.


Prerequisites

  • Working knowledge of Python is an asset, but is not required. There is an optional “Introduction to Python” module that you should review before beginning this course. Alternatively, you may consider taking the online workshop “Introduction to Python” that takes place from 26-29 August.
 
 
Software and Hardware Requirements
We will work with Python and Jupyter Notebooks in VS Code in the course, but you may use other text editors or IDEs if you prefer.


Schedule