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

level of course unit

Master course

Learning outcomes of course unit

Graduates are familiar with the functionality of advanced algorithms for data science and understand the statistical concepts behind the algorithms. Furthermore, they are able to select suitable algorithms for given problem areas. They are also familiar with the data structures, runtime specifications and complexity classes required by the algorithms.

prerequisites and co-requisites

Algorithmics & Statistics for Data Science 1

course contents

Students learn about advanced algorithms and underlying statistical procedures.

The following groups of algorithms are to be discussed:
-Neuronal networks
-Support vector machines
-Reinforced learning
-Genetic algorithms
-Representation learning
-Deep learning (RNN, CNN, etc.)
-Rule-based learning

Individual algorithms are presented by the respective groups or developed by stu-dents in group work.

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

Final examination

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

Huber Lukas , MSc

year of study


recommended optional program components


course unit code


type of course unit


mode of delivery

In-class course

work placement(s)



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