pb321 - Data Analytics in Times of Big Data (Complete module description)

No participant requirement 

The aim of the module is to teach basic analytical methods based on big data scenarios. The students from the humanities and natural sciences (e. g. social or environmental sciences, physics and mathematics) should be able to transfer current approaches to solving problems, which are used in particular for the use of in-memory computing and data science, to subject-specific questions and to work out solutions independently in small groups. The students have first experiences with the tasks of a Data Scientist.

Professional competences
The students:

• name and recognize the tasks of a Data Scientist.
• gain insight into current methods in the context of data analytics tasks.
• gain theoretical and practical knowledge in the process of data modeling and retrieval (extraction, transformation and data loading).
• can deal with subject-specific questions independently in small groups by means of the methods learned in the module.

Methodological competences
The students:

• carry out the tasks of a Data Scientist independently using mediated methods.
• learn the advantages and disadvantages of the different methods on the basis of their implementation and can use these methods in an optimized way on the basis of the acquired knowledge

Social competences
The students:

• carry out subject-specific questions in small groups.
• organize the tasks in the small groups and present their questions and results

Self-competences
The students:

• apply suitable methods of the Data Scientist and use them for technical questions.
• recognize tasks and assume responsibility for them

Due to the increasing volume of information and the constant development of information and communication technologies, companies and scientists are able to access information that conventional analysis systems can no longer process.

Therefore, it is imperative to develop an understanding of the methods and possibilities of a Data Scientist not only on the IT level, but also in the departments. Therefore, it is essential that the tasks and methods of a Data Scientist are imparted in practice.

The event is divided into two parts: (1) Introduction to the tasks/methods of a Data Scientist and necessary software systems and (2) practical implementation of the methods based on subject-specific questions in small groups and presentation of the results by the students.

Subject-specific questions can arise, for example, from research in the topics: Wind energy, pattern recognition in ecological growth structures, soil response and transport, social inequality, analysis of social networks, demography, analysis of complex social systems.

• Jensen, C., Pedersen, T. B., & Thomsen, C. (2010). Multidimensional databases and data warehousing. Morgan & Claypool Publishers.
• Loshin, D. (2012). Business intelligence: the savvy manager's guide. Newnes.
• Bramer, M. (2007). Principles of data mining. Springer.
• Leskovec, J., Rajaraman, A., & Ullman, J. D. (2020). Mining of massive data sets. Cambridge university press

http://www.wi-ol.de

In der veranstaltungsfreien Zeit, in der Regel 4 Wochen nach Ende des Vorlesungszeitraums.

Referat (max. 30 Min.) mit schriftl. Ausarbeitung (max. 15 Seiten) oder Hausarbeit (max. 25 Seiten)