Trends in Data Science (elective) (WP)*
Niveau
Master
Learning outcomes of the courses/module
The participants:
• are familiar with current thematic trends in the field of data science.
• are familiar with current technological developments in the field of data science.
• are familiar with current practical issues in the field of data science.
• are familiar with current thematic trends in the field of data science.
• are familiar with current technological developments in the field of data science.
• are familiar with current practical issues in the field of data science.
Prerequisites for the course
none
Course content
• 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
• 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/.
• 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
Semester hours per week
Planned teaching and learning method
Lecture with discussion, interactive workshop
Semester/trimester in which the course/module is offered
4