Trends in Data Science (elective) (WP)*
Niveau
Master
Learning outcomes of the courses/module
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 for the course
none
Course content
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 specialist literature
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
Seminar thesis
Language
English
Number of ECTS credits awarded
3
Share of e-learning in %
0
Semester hours per week
2.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Interactive workshop
Semester/trimester in which the course/module is offered
4
Name of lecturer
Prof. (FH) Dipl.-Inf. Karsten Böhm
Academic year
2
Key figure of the course/module
WPF.10
Type of course/module
integrated lecture
Type of course
Compulsory elective