Data Science & Intelligent Analytics PT
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Algorithmics & Statistics for Data Science 1 Lab

level of course unit

Master course

Learning outcomes of course unit

Graduates are familiar and competent in the functionality of fundamental algorithms for data science and understand the statistical concepts behind the algorithms. They are able to select and implement these algorithms within the context of a specific problem area.

prerequisites and co-requisites

not specified

course contents

The content of the integrative course “Algorithmics & Statistics for Data Science 1” is consolidated in the exercise by means of practical exercises. Acquired knowledge is discussed within the group, providing profound insights and a consolidation of the material that was theoretically discussed in the integrative course.

recommended or required reading

- Akerkar, R.; Sajja, P.S. (2016) Intelligent Techniques for Data Science. 1. Auflage, Springer, Berlin (ISBN: 978-3-319-29205-2).
- Bramer, M. (2017) Principles of Data Mining: undergraduate topics in computer science. 2. Auflage, Springer, London (ISBN: 978-4471-4884-5).
- Caffo, B. (2016) Statistical inference for data science. 1. Auflage, Leanpub, Victoria.
- Mahmood, Z. (2016) Data Science and Big Data Computing: Frameworks and Methodologies. 1. Auflage, Springer, Berlin (ISBN: 978-3319318592).
- Steele, B.; Chandler, J.; Reddy, S. (2016) Algorithms for Data Science. 1. Auflage, Springer, Berlin (ISBN: 978-3319457956).
- Witten, I.; Frank, E.; Hall, M.; Pal, C. (2016) Data Mining: Practical Machine Learning Tools and Techniques. 4. Auflage, Morgan Kaufmann, Burlington (ISBN: 978-0128042915).

assessment methods and criteria

-Seminar papers
-Final examination

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Group work
-Performing exercise tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Despotovic Miroslav , MA

year of study


recommended optional program components


course unit code


type of course unit


mode of delivery

In-class course

work placement(s)



Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
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