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) DI Dr. Martin Adam
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery