Data Science and Artificial Intelligence covers statistical and exploratory techniques that are used to make sense of the vast and complex data sets that have emerged in business. Data Science and Artificial Intelligence is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. Students learn to detect patterns in large data sets in quantitative and qualitative formats to translate them into actionable insights. The course covers five primary topics, but also touches upon other topics such as contemporary ethical concerns. It is complemented by Hands-on labs with Python.

• Data visualisation and exploration
•Supervised learning techniques for regression and classification
• Un- and self-supervised learning techniques
• Deep learning fundamentals
• Generative artificial intelligence including large language models

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.

After successful completion of the course, students will

Professional competence
• understand the basic concepts and methods of data science and artificial intelligence
• be able to assess the assumptions and quality of machine learning models

Methodological competence
• know and be able to select and apply the right models for a given task or data set
• be able to derive actionable insights from data mining results
• know basic visualisation and storytelling techniques

Social competence
• communicate effectively using visualisations
• understand different stakeholder perspectives in a data science project

Personal competence
• critically reflect on analytical outcomes
• improve and mitigate self-inflicted errors

Technological competence
• be able to use Python including their libraries such as scikit-learn and matplotlib to apply machine learning and to create visualisations

Written exam (90min)