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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.
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.
Resources
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.