Data Science and Analytics (DSA)

Understand Data: Collect, clean, and analyze data, apply statistics and artificial intelligence, and present results in a clear and meaningful way.

Contents

• Data acquisition and data collection
• Big data
• Data cleansing and preparation
• Data exploration and visualization
• Statistics and mathematics
• Machine learning
• Optimization and evaluation of models
• Ethics and data protection

Competencies

Students are able to

• Students will be able to identify the components that are part of the typical data science and data analysis process. These include the points mentioned in the course content as well as goal-oriented, analytical thinking and an approach to heterogeneous data. In addition, students will learn how to interpret data for their target audience and make it understandable.

Literature

• A. Azzalini and B. Scarpa, Data Analysis and Data Mining – An Introduction. Oxford, UK: Oxford University Press, 2012.
• A. Atzmueller, S. O. Olatunji, and J. Yao, Hrsg., Enterprise Big Data Engineering, Analytics, and Management. Hershey, PA, USA: IGI Global, 2016.

Lecturer

• Jacqueline Franßen

Recommended Previous Knowledge 

• working with code versioning systems (Gitlab, Github, etc.)
• collaboration in diverse teams & communication
• Documentation of software and architectures (UML) and the systematic use of diagram types
• Basic knowledge of Python, mathematics, and statistics is an advantage

Module Details

Semester	1
Lecture Language	German
Frequency	Not regularly
Credit Points (ECTS)	5
Course Coordinator	Prof. Dr. Sven Klaus
Duration	1 Semester
Course Achievement	None
Prerequisite for exam	None
Exam	Continuous Assessment (CA)

Weekly Hours (SWS) 

Lecture	2 SWS
Exercises	2 SWS
Sum	4 SWS

Work Load 

Lecture	50 h
Lab	50 h
Self Study	50 h
Sum	150 h