pb379 Data Science with Python (Complete module description)
| Module title | Data Science with Python | ||||||||||||
| Module code | pb379 | ||||||||||||
| Credit points | 6.0 KP | ||||||||||||
| Responsible institute | Institut für Biologie und Umweltwissenschaften (IBU) | ||||||||||||
| Responsible persons |
Module responsibility:
Michael Winklhofer
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| Prerequisites |
Stud.IP Registration |
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| Language of instruction | English | ||||||||||||
| Learning outcomes/competencies |
In-depth understanding of programming concepts in python Ability to write effective scripts for data analysis Application of machine learning for predictive modelling and efficient processing of big data Understanding of concepts of numerical mathematics, Application of python to computer simulation of physical problems |
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| Module contents |
Programming concepts in python; scientific modules numpy, scipy etc Machine learning: Regression, decision trees, random forests, neuronal networks Analysis of time series data and noise models Elements of numerical mathematics, numerical solution of differential equations |
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| Recommended reading | J.Grus, Data Science from Scratch – First principles with python. (O’Reilly) A.Geron, Hands on Machine Learning with scikit-learn and tensor flow (O’Reilly) A. Scopatz & K.D. Huff, Effective Computation in Physics – Field guide to research with Python (O’Reilly) |
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| Required assesment |
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| Duration (semesters) | 1 Semester | ||||||||||||
| Module frequency | jährlich | ||||||||||||
| Workload |
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| Further information | Aufnahmekapazität: Lehr-/Lernform: Links: |
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| Applicability of the module |
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