Algorithmics & Statistics for Data Science
level of course unit
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
Students learn about advanced algorithms and underlying statistical procedures.
The following groups of algorithms are to be discussed:
-Support vector machines
-Deep learning (RNN, CNN, etc.)
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
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
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