Scope: lecture (2 SWS) and mini project (2 SWS) / total 6 CP
Exam: The course includes a module examination consisting of the written examination (120 minutes) and the assessment of the online tests. The module grade is composed as follows: 60% written exam, 40% online tests. Registration for the exam is done via VIPA or HIS-LSF POS. Doctoral students may receive a certificate of attendance for the lecture if they pass the exercises, i.e. complete this work with a grade of at least 4.0.
Language of the course: English
Lecturer: Univ.-Prof. Dr.-Ing. Wolfgang Maass
Contact person: Maxx Richard Rahman email@example.com
Companies in research and industry use data to safeguard decisions and to offer data-intensive products and services. The competencies required for these processes are summarised under the term Data Science. The analysis of large amounts of data is composed of scalable data management, parallel algorithms, statistical modeling, and secure handling of the complex interaction of various instruments and platforms and is anchored in various disciplines. On the one hand, this lecture is intended to explain to the participants what is expected of future data scientists and on the other hand to give them the skills they need to fulfill these expectations. The methodical knowledge imparted in the course is intended to be a short “how-to” and enable the participants to independently decide when and why certain methods are to be used. Since one of the biggest problems in data analysis is often the wrong question, the lecture will also look at the company perspective to solve typical company problems and ask the right questions for suitable data analysis. The lecture presents concepts and instruments that are needed throughout the entire data science pipeline. In addition to the correct approach, the lecture will discuss the interpretation of the analysis results as well as their visualization and transformation into business models. In the accompanying exercises, presented methods and algorithms will be applied in practice, focusing on web programming, statistics, and the manipulation of data sets.
Registration starts: tbd
Registration deadline: tbd
Submission deadline: tbd
Video presentation of data-analytical service (5-7 min.)
Code repository (zip)
Report (3 pages)
Description of the problem statement, your database and your individual approach to the problem statement.
Please use the Template for Data Science Projects.
Description of code
Description of the data organization (like cleaning and preparation of the data).
Description of the architecture of the data.
Please include this point as the Appendix in the Template for Data Science Projects, to have both the report and the description of the code in one final document.
Note: If a project fails, the participants cannot pass the course as a whole.