Study program:

Academic level and semester:

Master, 3^rd semester

ECTS credits/workload per semester:

6 / 150

Contact hours per week/contact hours per semester:

4 / 45

Type/Teaching method:

Frequency:

Lecturer:

Prof. Dr. Lorenz Braun, Nabiha Javaid

Content:

The elective module aims to equip students with the skills and knowledge to prepare them for the data-driven culture in their respective professional areas. Regardless of the concerns, it cannot be denied that artificial intelligence is playing an increasingly important role in all fields. Hence, the aim of this module is to familiarize students with important concepts and applications of business analytics and artificial intelligence. The content of the module is broadly divided into three parts:
1. Learning the basics of descriptive analytics (Data Description).
2. For the artificial intelligence part, among other topics, students will learn the application of machine learning algorithms to economic issues (Introduction to Machine Learning).
3. Lastly, they will have the opportunity to learn from and connect with a skilled analyst to understand the technologies and the business, and the importance of using data to gain insights for informed decision-making (Applied Business Analysis).

Textbooks:

Lecture Script; Bluman, Allan G.: elementary statistics: a step by step approach (tenth edition); A. I. Tolulope: Data Science and Analytics for SMEs (Apress); Chiu, Yu-Wei (David Chiu): Machine Learning with R Cookbook (Packt >, current edition); Chollet, F. und J. J. Allaire: Depp Learning mit R und Keras – Das Praxishandbuch (mitp Professional, current edition); Gooapudi, S.: Practical Machine Learning (Packt>, current edition); James, G. et al.: An Introduction to Statistical Learning – with Applications in R (Springer, current edition); Lantz, B.: Machine Learning with R – Expert Techniques for Predictive Modeling (Packt>, current edition)

Restrictions:

Assessment: