851-0585-38L  Data Science in Techno-Socio-Economic Systems

Courses

Catalogue data

Abstract	This course introduces how techno-socio-economic systems in our complex society can be better understood with techniques and tools of data science. Students shall learn how the fundamentals of data science are used to give insights into the research of complexity science, computational social science, economics, finance, and others.
Learning objective	The goal of this course is to introduce students to data science methods that help understanding of complex systems, often encountered in techno-socio-economic settings. By the end of the course, the students will be able to: 1. Formulate testable hypotheses about techno-socio-economic systems. 2. Apply methods from statistics, data science, or machine learning to test these hypotheses. 3. Presenting their research findings clearly, using appropriate visualisations and reporting standards.
Content	Will be provided on a separate course webpage.
Lecture notes	Slides will be provided.
Literature	Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media. https://dl.acm.org/doi/10.5555/2904392 Mehta, P., Bukov, M., Wang, C. H., Day, A. G., Richardson, C., Fisher, C. K., & Schwab, D. J. (2019). A high-bias, low-variance introduction to machine learning for physicists. Physics reports, 810, 1-124. https://www.sciencedirect.com/science/article/pii/S0370157319300766 Chakrabarti, A., Bakar, K., & Chakraborti, A. (2023). Data Science for Complex Systems. Cambridge: Cambridge University Press. doi:10.1017/9781108953597 Link Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press. https://www.databookuw.com/ Further literature will be recommended in the lectures.
Prerequisites / Notice	Substantial programming skills and knowledge of basic statistical methods are expected. We recommend this course for students in the 4th semester or above. Students need to present a new project, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies assessed Problem-solving assessed Project Management assessed Social Competencies Communication assessed Cooperation and Teamwork assessed Customer Orientation fostered Leadership and Responsibility assessed Self-presentation and Social Influence assessed Sensitivity to Diversity assessed Negotiation fostered Personal Competencies Adaptability and Flexibility assessed Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics assessed Self-awareness and Self-reflection assessed Self-direction and Self-management assessed

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	3 credits
Examiners	D. Helbing, D. Carpentras, V. Vasiliauskaite
Type	graded semester performance
Language of examination	English
Repetition	Repetition only possible after re-enrolling for the course unit.

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
Computer Science Bachelor	Minor Courses	W
Spatial Development and Infrastructure Systems Master	Recommended Electives of Master Degree Programme	W
Science, Technology, and Policy Master	Electives	W
Science in Perspective	D-INFK	W
Science in Perspective	Sociology	W
Science in Perspective	D-ITET	W
Science in Perspective	D-MTEC	W
Science in Perspective	D-PHYS	W