Lecturer:

Prof. Dr. Claudius Steinhardt 

Workload:

150 hours; Contact hours: 36h; Self-study: 114 hours  

Content: 

• Introduction to Business Analytics
• Data Preprocessing & Exploratory Analytics
• Methods of Classification
• Clustering & Association Rules

Learning outcomes: 

• Students will have a broad overview of the different aspects of the field and be theoretically competent in dealing with the challenges of business analytics 
• Students will have basic theoretical knowledge of different particular methods of data mining for business analytics, being able to analyze their potential and their individual strengths/weaknesses depending on the given task 
• Based on the theory, students will be enabled to systematically and adequately apply state-of-the-art software to solve business analytics tasks 

Proof of performance:

Written examination 

Bibliography: 

• Larose, D., Larose, C.: "Discovering Knowledge in Data: An Introduction to Data Mining", Wiley (current edition).
• Larose, D., Larose, C.: "Data Mining And Predictive Analytics", Wiley (current edition).
• Shmueli, G., Bruce, P., Patel, N.: "Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner", Wiley (current edition).