Trends in Data Science (elective)
level of course unit
Learning outcomes of course unit
The following learning outcomes are developed in the course:
- Students are familiar with current thematic trends in the field of data science.
- Students are familiar with current technological developments in the field of data science.
- Students are familiar with current practical issues in the field of data science.
prerequisites and co-requisites
The contents of this course are not set, but will be adapted to the current prevailing trends. Content examples may include:
- New technologies in the field of Big Data Processing
- Trends in programming languages in data analysis
- New concepts of data processing (e.g. Data Lake)
- New questions in the field of data science research
- New questions in data science practice
recommended or required reading
Due to the changeability of the content, only a few web sources are listed here as examples, which are currently strongly represented in the area of Data Science Trends:
- Medium (2020): Towards Data Science (Ed. 1), online, https://towardsdatascience.com/.
- KDNuggets (2020): Knowledge Discovery Nuggets (Ed. 1), online, https://www.kdnuggets.com/.
assessment methods and criteria
language of instruction
number of ECTS credits allocated
eLearning quota in percent
planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Interactive workshop
semester/trimester when the course unit is delivered
name of lecturer(s)
Prof. (FH) Dipl.-Inf. Karsten Böhm
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery