Algorithmics & Statistics for Data Science 1 Lab
level of course unit
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
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
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
-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