Inhalt
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Lern- und Qualifikationsziele
The course is intended to prepare the students for working hands-on in the area of Data Science and Instrumental Data Analysis.
Voraussetzungen
Bachelor of Science
Gliederung / Themen / Inhalte
Practical Instrumental Data Analysis and Data Science
- Intro to software tools for Data Science and Instrumental Data Analysis
- Basic introduction to programming - e.g. Python
- Programming environments for Data Science - Jupyter Lab or similar
- Working with datasets
- The scikit-learn toolkit
- Case studies in Data Science: Regression Analysis, Principal Component Analysis, Multivariate Data Analysis and Clustering
The topics to be discussed here will be oriented along the lecture "Data Science and Instrumental Analysis" parallely offered as part of this Module KM4
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| Bemerkung |
Ansprechpartner
Prof. Kannan Balasubramanian, AES 5-9, R.202
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| Prüfung |
Klausur (90 min) oder mündliche Prüfung (ca. 45 min) oder multimediale Prüfung (ca. 30 min) über den Inhalt des gesamten Moduls |
