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

level of course unit

Master course

Learning outcomes of course unit

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

prerequisites and co-requisites

not specified

course contents

Students learn about basic algorithms and the underlying statistical procedures.

The following groups of algorithms are to be discussed:
- Statistical measured values (point and interval estimator)
- Statistical test procedures
- Grouping algorithms
- Decision trees
- Random forests
- Regression algorithms
- Naive Bayes
- Associative algorithms
- Inductive logical programming
- Algorithms for dimension reduction (e.g. PCA)

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)

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)



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Prof. (FH) Dipl.-Informatiker Karsten Böhm
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