Contents of the subject

•    Revision of basic statistics knowledge
•    Data acquisition techniques
•    Writing a proposal
•    Introduction to computer programm
•    Importing data from sources / exporting data
•    Generation and testing hypotheses
•    Classification / Patterns
•    Time Series Analysis
•    Geo computation
•    Text analysis / Natural language processing
•    Image processing
•    Machine Learning
The course focuses on making better decisions analyzing data from real applications. In a first step, the course will present new concepts and developments in the mentioned topics. In a second step, the students will apply the above concepts to real world data sets. 

Qualification aims of the subject

The general purpose of the course is to provide students with basic concepts of analyzing data sets.. Furthermore, this course is designed to connect concepts as well as theoretical ideas with the “real” world. This transfer should also support the critical discussion of new concepts. 

Applicability of the subject

Preparation for the Master Thesis

Master programs: All programmes

The nature of the examination / requirements for the award of credit points

The assessment is a combined examination consisting of 60% written assignment and 40% oral presentation. 
•    The written assignment requires students to apply theoretical concepts to a real case (ILO 1, 2, 3, 4 5)  6000 words
•    The oral presentation requires students to present their findings in class (ILO 5, 6)

Literature and learning resources

•    Ahmed, M., Khan Pathan, A. (latest ed.): Data Analytics – Concepts, Techniques, and Applications, CRC Press, Taylor and Francis Group
•    Kudyba, St. (latest ed.): Big Data, Mining, and Analytics, CRC Press, Taylor and Francis Group
•    Liebowitz, J. (latest ed.): Big Data and Business Analytics, CRC Press, Taylor and Francis Group
•    Mathematica Online Documentation, https://reference.wolfram.com/language/
•    Further literature will be introduced at the beginning of class.

Teaching and learning forms

• (ILO 1): Obtain a comprehensive understanding of statistical properties of data and techniques to visualize them.
• (ILO 2): Obtain a comprehensive understanding of computer programmes to handle data.
• (ILO 3): Work with scientific articles and books.
• (ILO 4): Learn to make appropriate decisions how to connect data and methods.
• (ILO 5): Learn to work effectively and efficiently in a team situation.
• (ILO 6): Develop oral communication skills as well as presentation skills.

Participation requirements

Profound knowledge of statistics