This course aims to explore fundamental topics of the social sciences using computational methods including network science, text analysis, and machine learning. Participants will learn how to apply and evaluate these methods to measure and understand social and economic outcomes. Students will also gain experience in various methods to collect data from the web. We will use the Python programming language and the Jupyter Notebook environment.

After completing this course students will have an understanding of the opportunities and pitfalls of studying social behavior using data collected from the web. Students will understand how to formulate and test hypotheses using digital trace data. Students will apply mathematical and statistical tools to support design making. Completing this course will contribute to the students’ ability to efficiently work and communicate in a team to deliver interpretable and practical information from big data. They will also reinforce their understanding of ethical data analysis and potential biases in data-driven analyses of social phenomena.

The rules on the attendance of a Continuous Assessment Course (PI) apply. See the dedicated page on the WU portal for further information.

Each course session will consist of a lecture introducing a specific topic and methodology and a hands-on programming lab, implementing the main ideas of the lecture in code.

The final grade will be composed of:

- (40%) a mid-term assignment of programming and analysis tasks, to be submitted individually as an annotated Jupyter Notebook

- (50%) a final assignment, involving programming, analysis, and written components, to be submitted in pairs as an annotated Jupyter Notebook

- (10%) to be earned by active participation during the lectures and practical sessions.

The participation points (10%) will be granted by participation (not correctness) in short clicker quizzes during the lecture. Attendance of the online lectures is a requirement and missed clicker quizzes cannot be made up.

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).