Description:

This course offers a structured, practice-oriented introduction to Data Science, highlighting how Data Science challenges are addressed in real-world business contexts. Students learn the complete Data Science lifecycle, from understanding business problems and data, through data preparation and modeling, to evaluation and deployment, following established standards and best practices. Through applied examples, the course covers essential methods and techniques for tackling regression, classification, and clustering problems. Particular emphasis is placed on understanding the strengths and limitations of these methods and techniques for given tasks and contexts.

Learning Targets:

Students

• learn about fundamental concepts and practices of Data Science
• understand the Data Science lifecycle with regard to business and data understanding, data preparation, modeling, evaluation, and deployment
• apply selected methods and techniques, and assess their advantages and disadvantages
• can solve regression, classification, and clustering problems based on practical examples and best practices

Outline:

• Introduction to Data Science
• Data Science Fundamentals
• Data Preparation and Exploration
• Regression and Classification
• Decision Trees
• Ensemble Methods
• Naive Bayes
• Support Vector Machines
• Clustering
• Standards and Best Practices

Methods of Assessment:

Written exam (60–90 minutes) and additional in-semester tests may be required for exam eligibility (Prüfungszulassung). Whether such tests are required will be announced at the beginning of the course.