| Department of Computing Science |
6 KP |
- module responsibility
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- Prüfungsberechtigt
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| Module components |
Semester courses Summer semester 2024 |
Examination |
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Lecture
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2.01.040 - Data Science I
- Prof. Dr. Wolfram Wingerath
Tuesday: 16:00 - 18:00, weekly (from 02/04/24)
Thursday: 16:00 - 18:00, weekly (from 11/04/24)
Dates on Tuesday, 09.07.2024 12:30 - 18:00, Wednesday, 10.07.2024 10:00 - 18:00, Tuesday, 24.09.2024 13:00 - 18:00, Wednesday, 25.09.2024 10:00 - 18:00, Friday, 27.09.2024 14:00 - 17:00
Data Science is an interdisciplinary science at the intersection of statistics, machine learning, data visualization, and mathematical modeling. This course is designed to provide a practical introduction to the field of Data Science by teaching theoretical principles while also applying them practically. Topics covered range from data collection and preparation (data sources & formats, data cleaning, data bias), mathematical foundations (statistical distributions, correlation analysis, significance) and methods for visualization (tables & plots, histograms, best practices) to the development of models for classifying or predicting values (linear regression, classification, clustering).
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Exercises
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2.01.040 - Data Science I
- Prof. Dr. Wolfram Wingerath
Tuesday: 16:00 - 18:00, weekly (from 02/04/24)
Thursday: 16:00 - 18:00, weekly (from 11/04/24)
Dates on Tuesday, 09.07.2024 12:30 - 18:00, Wednesday, 10.07.2024 10:00 - 18:00, Tuesday, 24.09.2024 13:00 - 18:00, Wednesday, 25.09.2024 10:00 - 18:00, Friday, 27.09.2024 14:00 - 17:00
Data Science is an interdisciplinary science at the intersection of statistics, machine learning, data visualization, and mathematical modeling. This course is designed to provide a practical introduction to the field of Data Science by teaching theoretical principles while also applying them practically. Topics covered range from data collection and preparation (data sources & formats, data cleaning, data bias), mathematical foundations (statistical distributions, correlation analysis, significance) and methods for visualization (tables & plots, histograms, best practices) to the development of models for classifying or predicting values (linear regression, classification, clustering).
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Hinweise zum Modul |
| Prerequisites |
Basics of databases, Python programming and statistics |
| Prüfungszeiten |
At the end of the lecture period or by arrangement with the instructor. |
| Module examination |
Written or oral exam or portfolio or project or practical exercise |
| Skills to be acquired in this module |
The module teaches fundamentals from the field of Data Science, covering purposes, challenges, and common best practices.
Professional competences The students - have knowledge of basic concepts, problems and solution approaches from the field of Data Science.
- are able to justify the choice of specific data analysis methods for a given problem
- include possible imponderables in the analysis when evaluating analysis results
Methological competences
The students - are able to translate questions from a specific domain into a feasible analysis
- work on Data Science tasks to expand their understanding of the different approaches and methods.
Social competences
The students - discuss approaches and problems encountered in smaller and larger groups
Self competences
The students - reflect on their actions when identifying possible solutions and critically question their own results
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