Data Science & Intelligent Analytics PT
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Data Science for the Natural Sciences

level of course unit

Master course

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

not specified

course contents

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

language of instruction

English

number of ECTS credits allocated

3

course-hours-per-week (chw)

2

planned learning activities and teaching methods

Lecture with discussion
Group work
Performing exercise tasks

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Head of studies

year of study

2

recommended optional program components

N.A.

course unit code

VT.1

type of course unit

ILV

mode of delivery

In-class course

work placement(s)

N.A.

Kontaktpersonen

Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
Karsten.Boehmfh-kufstein.ac.at
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Förderer

Mit Unterstützung von Bund, Land und Europäischer Union: