GESIS Training Courses

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Noemi Hartung
Tel: +49 621 1246-211

Automated Image and Video Analysis with Python

Mannheim B6, 4-5
Course duration:
09:00-16:00 CEST
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Andreu Casas, Felicia Loecherbach

About the lecturer - Andreu Casas

About the lecturer - Felicia Loecherbach

Course description

Social scientists have long argued that images play a crucial role in shaping and reflecting political life. This role is heightened by the bombardment of images that people experience today through many communications channels, from television to social media. Digitization has both increased the presence of images in daily life and made it easier for scholars to access and collect large quantities of pictures. However, using images collected in observational settings as data for social science inference is an arduous task. Fortunately, recent innovations in computer vision, the subfield of computer science concerned with automated image analysis, can reduce the costs of using images as data.
In this course, we'll dig into the necessary theoretical and methodological expertise needed to apply machine learning methods to address social science questions. We will combine theoretical sessions where we'll discuss research using computer vision methods for the study of politics, communication science, etc.; with sessions where we'll cover in details key methodological advances needed to fully understand state-of-the-art computer vision methods (deep learning, neural networks, convolutional neural networks, etc.), as well as practical sessions where we'll go over several python tutorials implementing different computer vision techniques, for image processing (e.g. splitting videos into analytical frames), object and face detection, image (supervised and unsupervised) classification, facial trait analysis, etc.
Students with basic programming skills/experience in Python and some machine learning background will get the most out of the course. However, we'll also take the time to briefly review some key machine learning concepts necessary to implement machine learning methods, and students will be provided with clear and easy-to-follow sample code for each of the practical tutorials. By the end of the course, students will have a good understanding of the kind of research questions that can be answered using computer vision methods, as well as a good understanding of several techniques and how to apply them for their own research.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.

Target group

Participants will find the course useful if:
  • they are PhD students, early career scholars, industry professionals, or generally interested in using computational methods to automatically analyze large quantities of video/image data.

  • Learning objectives

    By the end of the course participants will:
  • have a good overview of the existing images-as-data literature in the social sciences
  • have a good understanding of key deep learning concepts relevant for the implementation of computer vision methods
  • have a good understanding of several computer vision techniques (object and face detection/recognition, image classification, facial trait analysis, etc.)
  • have a good understanding of the many options and techniques available to store and compute visual data
  • be able to implement different computer vision techniques in Python
  • be able to use/adapt different computer vision techniques for their own research projects
    Organisational Structure of the Course
    The course will be organized around three different types of sessions:
  • Lectures in which the instructors will present relevant literature, theory, concepts, and methods; and discuss them with the students.
  • Tutorials in which the instructors will provide, run, and discuss, sample code designed to implement different computer vision techniques. Students will also run the code on their own and can ask as many clarifying questions as needed.
  • Consulting sessions in which students will work on implementing the learned techniques on new data and projects. The instructors will provide students with new sample data, and will help them adapt the sample code from the tutorials so that it works for the new data. During these sessions, students can also bring their own data and ask questions about how to adapt the sample code for their own project, or what additional computer vision methods can help them answer their substantive questions of interest.
    The lectures will take place in the morning of the first three days (Monday-Wednesdays). Tutorials will take place in the afternoon of the first three days, as well as at different times during the last two days. The consulting sessions will also take place at different times during the last two days.


  • basic programming skills/experience in Python
  • basic machine learning knowledge (e.g. distinction between supervised and unsupervised learning, familiar with the training process in machine learning - such as train/test/validation split, cross-validation, etc. - although these concepts will be reviewed in more detail during the course)
  • a Google account: we will use Google Colab in the course tutorials.
  • For those who would like a primer or refresher in Python, we recommend taking the online workshop “Introduction to Python” that takes place from 04-06 September 2023.
    Software and hardware requirements
    Participants should bring their own laptop for use in the course.
    The course will use Google Colab, so participants need a Google account. There is no need to install Python locally.
    Monday, 18.09.
    Morning Session
  • Introduction
  • Lecture 1. Introduction to Images as Data in Social Science Research
  • Lecture 2. Introduction to Neural Nets and Computer Vision
  • Afternoon Session
  • Tutorial 0. Technical Setup and Python Refresher
  • Tutorial 1. Image Processing
  • Lecture 3. Supervised Image Classification
  • Tuesday, 19.09.
    Morning Session
  • Catching-up Moment
  • Lecture 4. Unsupervised Image Classification
  • Tutorial 2. Supervised Image Classification
  • Afternoon Session
  • Tutorial 3. Unsupervised Image Classification
  • Lecture 5. Multimodal Modeling
  • Presentation/discussion of the Data Challenge in days 3 & 4
  • Wednesday, 20.09.
    Morning Session
  • Catching-up Moment
  • Preparation for Data Challenge
  • Lecture 6. Ethics and Research Practices in Computer Vision Research
  • Tutorial 4. Multimodal Modeling
  • Afternoon Session
  • Literature Discussion
  • Tutorial 5. Face Detection, Recognition, and Analysis
  • Thursday, 21.09.
    Morning Session
  • Intro and Making up the Groups
  • Work Group 1. Question, Research Design, and Methods
  • Participants work on their projects (instructors are present to answer questions and help out)
  • Afternoon Session
  • Participants work on their projects (instructors are present to answer questions and help out)
  • Work Group 2. Project Check-In.
  • Individual Consult 2. Project Check-In
  • Friday, 22.09.
    Morning Session
  • Work Group 3. Project Check-In and Discuss Presentation.
  • Individual Consult 3. Project Check-In and Discuss Presentation
  • Participants work on preparing the presentation for the afternoon (instructors are present to answer questions and help out)
  • Afternoon Session
  • Project Presentations and Q&A/Discussion/Feedback