Evidenzorientierte Entscheidungen auf der Grundlage von Big Data Analytics, virtuell (0000001981)

week 17 to week 26 (flexible start, exam on June 28)

 Course Demo

After a successful participation, the students are able …

1. to differentiate characteristics of big data analysis with traditional business theory/decisions/methods (in particular concerning data creation, data storage, data processing),
2. to identify (operational) opportunities for big data analytics in business and management,
3. to apply basic methods of data collection, processing, and evaluation,
4. to recognize challenges in big data analytics (e.g. privacy, data security, ethical considerations, etc.),
5. to identify and use trends and developments in big data analytics for their later career.

The module will start with a virtual webinar, whereas students will get access to the learning platform, which contains the electronic learning contents in the form of interactive lecture notes and course-specific data sets. Other materials are video tutorials and data analysis tools, which help to acquire core competencies for big data analysis projects. In the accompanying case studies, the basic data processing involved in big data analysis are taught. For this purpose, a webinar explains these central steps in a comprehensible manner and motivates the students through immediate application and independent learning. For the case-study-specific evaluations, the students rely on R and have to evaluate various data sets using the methods described and graphically visualize their results. The case studies contain the following elements and will increase in difficulty during the semester, while each case study has a specific focus:

1. Economic theory/questions (including practitioner interviews)
2. Data preparation and exploratory data analysis
3. Purposeful data processing (model estimation and analysis)
4. Interpretation of the results with regard to 1. (but also dangers of the analyzes, e.g. spurious correlations or ethical aspects associated with the analyzes, etc.)

Regarding bullet point 4, additional online services are provided and students are encouraged to discuss ethical issues online. At the end of the semester, an outlook is given. This should enable the teachers to be able to set specific priorities in this rapidly developing field. Finally, a questionnaire-based evaluation will also be provided to measure the learning results in the context of big data analysis by a self-assessment.