Registration via LPIS
|Wednesday||10/12/22||09:00 AM - 01:00 PM||TC.3.08|
|Wednesday||10/19/22||09:00 AM - 01:00 PM||TC.4.15|
|Wednesday||11/02/22||09:00 AM - 01:00 PM||TC.4.15|
|Wednesday||11/09/22||09:00 AM - 01:00 PM||D4.0.136|
|Wednesday||11/16/22||08:00 AM - 12:00 PM||TC.5.28|
|Wednesday||11/23/22||09:00 AM - 01:00 PM||TC.3.08|
This course aims to explore fundamental topics of the social sciences using computational methods including network science, text analysis, and machine learning. Following a review of core statistical concepts, students will learn to handle data in the form of text and networks and how to extract information from them. Participants will learn how to apply and evaluate these methods to measure and understand social scientific phenomena. 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 in the form of text and networks. Students will understand how to formulate and test hypotheses using such data. Students will apply mathematical and statistical tools to these kinds of data 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 (present at least 80% of the time). 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).Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.