Seminar: 5.04.4066 Python for data and complex systems scientists (Blockveranstaltung) - Details

Seminar: 5.04.4066 Python for data and complex systems scientists (Blockveranstaltung) - Details

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Course name Seminar: 5.04.4066 Python for data and complex systems scientists (Blockveranstaltung)
Subtitle
Course number 5.04.4066
Semester WiSe24/25
Current number of participants 26
expected number of participants 40
Home institute Institute of Physics
Courses type Seminar in category Teaching
Type/Form SE/PRA
Pre-requisites Basic knowledge of mathematical methods in physics
Basic knowledge of python programming is helpful but not required
Learning organisation Students acquire extensive knowledge of working with a variety of data sets (experimental and numerically generated time series, random fields, geospatial data) in Python (e.g., through matrices, data frames, and numpy pandas). The data sets are discussed in the context of the corresponding nonlinear systems (e.g., turbulent flows, urban systems), leading to a solid background in complex systems theory (dynamical systems, sensitivity to initial conditions, fractals). Basic to advanced statistical analysis methods (e.g., machine learning methods using Scikit-Learn and Tensorflow), as well as fundamentals for exploratory spatial data analysis (spatial econometrics) and dimensionality reduction techniques (t-distributed stochastic neighbor embedding T-SNE, principal component analysis PCA, self-organizing maps), as well as clustering techniques, are discussed.
Lehrsprache englisch
Miscellanea Literatur:
• Argyris, J. H., Faust, G., Haase, M., & Friedrich, R. (2015). An exploration of dynamical systems and
chaos: completely revised and enlarged second edition. Springer.
• Haken, H., Synergetik: Eine Einführung. Nichtgleichgewichts-Phasenübergänge und
Selbstorganisation in Physik, Chemie und Biologie. Springer-Verlag, 2013.
• Risken, H., Fokker-Planck equation. Springer Berlin Heidelberg, 1996.
• Strogatz, S. H., Nonlinear dynamics and chaos with student solutions manual: With applications to
physics, biology, chemistry, and engineering. CRC press, 2018.
• Mitchell, M., Complexity: A guided tour. Oxford university press, 2009.
• VanderPlas, J., Python data science handbook: Essential tools for working with data. " O'Reilly
Media, Inc.", 2016.
ECTS points 3

Rooms and times

Module assignments

Comment/Description

Die Veranstaltung wird als Blockveranstaltung am Ende des Semesters angeboten werden.

Complex systems such as bird flocks, the dynamics of the brain, or stock markets are typically characterized by a large number of individual components interacting with each other to produce complex collective behavior. This course is devoted to the analysis of data originating from such complex systems using statistical methods, e.g., state-of-the-art machine learning methods, as well as spatio-temporal data analysis. The goal of the course is to gain a deeper understanding of the mechanisms driving complex behavior using a practical data science approach. In addition, stochastic modelling (e.g., using random or synthetic fields, Langevin equations, master or Fokker-Planck equations) will be introduced to emulate complex behavior and reproduce statistical features from the preceding data analysis.