Business Analytics
Overview and learning objectives
In an increasingly digitalised world, companies are faced with the challenge of making sense of huge amounts of data from social media, online communities and traditional databases. The targeted analysis of this data opens up valuable insights, improves decision-making processes and creates competitive advantages in areas such as marketing, product development and customer management. At the same time, digital transformation - whether through the further development of existing business models or the creation of new, technology-driven approaches - requires a deep understanding of modern analytical methods.
The "Business Analytics" module teaches important fundamentals of technology, data and process management as well as business model development with a focus on business analytics. Key data analytics methods are introduced and their practical application illustrated in order to emphasise their importance for the digital transformation of companies.
Lecturers
Course information
Students who have successfully completed this module will be familiar with the essential theoretical principles and methods of digital business and business analytics. In addition, they will be familiar with the fundamentals and concepts of machine data analysis, as well as its opportunities and risks, and will be able to systematically analyze data using machine data analysis methods, interpret the results, and derive recommendations for action.
This module covers the following technical content:
- Fundamentals of digital business
- Technology management
- Data management
- Business analytics and Python
- Process management and simulation
- Business model development
- Digital evolution
- Digital revolution
- Fundamentals of machine data analysis
- Concepts, methods, and practical implementation of machine data analysis
- Visualization of data and data analysis results
- Practical application of machine data analysis
- Hoffmeister, C. (2017). Digital Business Modelling: Digitale Geschäftsmodelle entwicklen und strategisch verankern. (2) Carl Hanser Verlag, S. 371.
- Osterwalder, A (2011). Business Model Generation: Ein Handbuch für Visionäre, Spielveränderer und Herausforderer. Campus Verlag, S. 285.
- Rogers, D. L. (2016). The digital transformation playbook: Rethink your business for the digital age. Columbia University Press.
- Krcmar, H. (2005). Informationsmanagement. Springer
- Laudon, K.C.; Laudon, J.P. (2017). Management Information Systems – Managing the Digital Firm. Pearson HigherEducation.
- Seiter, M. (2017). Business Analytics. Vahlen
- Evans, J.R. (2016). Business Analytics – Methods, Models, and Decisions. Pearson.
- Bramer, M. (2020). Principles of Data Mining. 4. Auflage, Springer, London.
- Weisberg, S. (2005). Applied Linear Regression. 3. Auflage, John Wiley & Sons, Hoboken.
- Witten, I. H., Frank, E., Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. 3. Auflage, Morgan Kaufmann, Amsterdam.
Organizational information
Next course start: SoSe 26
Place: tbd
Time: tbd
ECTS: 6
Lecture (2 SWS) with Exercise (2 SWS)
Credit points are awarded on the basis of passing the written module examination. Registration for this examination does not require any prior proof of academic achievement.
The module grade corresponds to the result of the module examination.
Focus: Business Analytics, Technologie- und Prozessmanagement, Wahlpflicht BWL
Programs: B.Sc. Wirtschaftswissenschaften, B.Sc. Wirtschaftsmathematik, B.Sc. Wirtschaftschemie, B.Sc. Wirtschaftsphysik and Studiengänge mit Nebenfach Wirtschaftswissenschaften