Data Science for the Natural Sciences
level of course unit
Learning outcomes of course unit
Students are familiar with fundamental usage areas of data acquisition, data storage, data analysis and data usage within the context of natural science applications. They understand the special challenges of this usage area and are familiar with established best practice methods. Furthermore, they are able to independently design and implement data-based applications in this area while taking domain-specific requirements into account.
prerequisites and co-requisites
Students acquire fundamental knowledge of techniques and tools of data science in the area of natural sciences. They specifically learn about techniques and applications in the following areas:
-Biology (e.g. genome research, medical diagnostic procedures, etc.)
-Physics (e.g. object recognition by means of image data processing, etc.)
-Chemistry (e.g. processing data-intensive experiments, etc.)
The purpose of this course is to give students special insight into other areas of data processing and expand their problem-solving horizon.
recommended or required reading
- Cady, F. (2017) The Data Science Handbook. 2. Auflage, Wiley, Hoboken (ISBN: 978-1119092940).
- Hütt, M.-T.; Dehnert, M. (2016) Methoden der Bioinformatik: Eine Einführung zur Anwendung in Biologie und Medizin. 2. Auflage, Springer Spektrum, Heidelberg (ISBN: 978-3662461495).
- Selzer, P. M.; Marhöfer, R. J.; Koch, O. (2017) Angewandte Bioinformatik: Eine Einführung. 2. Auflage, Springer Spektrum, Heidelberg (ISBN: 978-3662541340).
assessment methods and criteria
Seminar papers or final examination
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number of ECTS credits allocated
planned learning activities and teaching methods
Lecture with discussion
Performing exercise tasks
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