ERP Systems & Business Process Management PT
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Trends in Data Science (elective)*

level of course unit

Master

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

none

course contents

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

Aufgrund der Veränderlichkeit der Inhalte werden hier nur beispielhaft einige Web-Quellen angeführt, die derzeit im Bereich Data Science Trends stark vertreten sind:
- 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 of instruction

German

number of ECTS credits allocated

3

eLearning quota in percent

0

course-hours-per-week (chw)

2

planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Interactive workshop

semester/trimester when the course unit is delivered

4

name of lecturer(s)

Prof. (FH) DI Dr. Martin Adam

year of study

2

recommended optional program components

none

course unit code

WPF.10

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

none