pb379 - Data Science with Python (Complete module description)
| Module label | Data Science with Python |
| Module code | pb379 |
| Credit points | 6.0 KP |
| Workload | 180 h |
| Institute directory | Institute for Biology and Environmental Sciences |
| Applicability of the module |
|
| Responsible persons |
|
| Prerequisites | Stud.IP Registration |
| Skills to be acquired in this module | 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 |
| 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 |
| 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) |
| Links | http://scipy-lectures.org/intro/index.html |
| Language of instruction | English |
| Duration (semesters) | 1 Semester |
| Module frequency | annually |
| Module capacity | 30 |
| Type of module | Ergänzung/Professionalisierung |
| Module level | PB (Professionalisierungsbereich / Professionalization) |
| Teaching/Learning method | Lectures and supervised exercises |
| Previous knowledge | Basic knowledge in mathematics (e.g., algebra, analysis) and physics |
| Type of course | Comment | SWS | Frequency | Workload of compulsory attendance |
|---|---|---|---|---|
| Lecture | 4 | WiSe | 56 | |
| Exercises | 3 | WiSe | 42 | |
| Total module attendance time | 98 h | |||
| Examination | Prüfungszeiten | Type of examination |
|---|---|---|
| Final exam of module | Nach Ankündigung |
Programming assignments / Fachpraktische Übungen (max. 12 Programmieraufgaben) |
