Business Analytics: Technologies, Methods, and Concepts

Course content
Business Analytics encompasses a wide range of methodological and technological approaches for the analytical evaluation of business-relevant data from different source systems in order to gain insights into past, present, and future business activities. Of interest, for example, are aggregated or filtered insights into company performance, or the discovery of previously unknown relationships, trends, and patterns, in order to generate new knowledge and improve decision support within the organization.
To this end, the approach draws on various methods from diverse fields such as statistics, data mining, and artificial intelligence. The practice-oriented course introduces the fundamentals of the subject and provides an overview of relevant concepts, methods, and technologies. The focus is particularly on the subfield of Predictive Analytics and the approaches of (supervised) machine learning for building predictive models.
Using a systematic process model, the fundamental steps and principles of predictive modeling are illustrated and supported with example approaches (e.g., model training using deep neural networks). The course consists of a lecture for conveying conceptual content and an accompanying computer-based exercise, in which selected aspects are explored in greater depth and exemplarily implemented using the Python programming language with demonstration examples.

Learning objectives and competences
 
Students…

• know the application areas of Business Analytics and are able to classify fundamental technologies, methods, and concepts,
• are able to name basic terms of predictive modeling and (supervised) machine learning,
• are able to explain, on the basis of a systematic procedure, the fundamental steps for building domain and data understanding, for the exploration and preprocessing of data, as well as for the development and evaluation of predictive models,
• master the fundamental procedures and principles of predictive modeling and are able to apply them to various practical examples and to evaluate, interpret, and critically question the results,
• are able to implement approaches of data analysis and machine learning for the development of predictive models in Python.

Requirements for participation
Basic knowledge in the modules Data Science: Data Analysis and Data Science: Statistics. Basic programming knowledge (e.g., of loops, variables, functions, etc.) is recommended. The number of participants is limited. Details on course registration can be found on the website.

Dates

The lecture takes place in person on Tuesday from 08:00 to 09:30. The first session is on 14.10.2025 in room TBA.

The exercise takes place in person on Tuesday from 10:00 to 11:30. The first session is on 14.10.2025 in room TBA.

All dates can be found in the corresponding course module on StudOn.

Further links and StudOn