Art:Modul
ECTS-Credits:6.0
Plansemester:1
Lektionen / Semester:52.0 L / 39.0 h
Selbststudium:141.0 h
Modulleitung/Dozierende
- Arianna Casanova Flores, Ph. D.
(Modulleitung)
Studiengang
Masterstudium Wirtschaftsinformatik (01.09.2019)Lehrveranstaltungen
Beschreibung
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
Lernergebnisse
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
Kompetenzen
Lehrmethoden
• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Voraussetzungen (inhaltlich)
Basic knowledge of statistics and linear algebra is recommended.
Literatur
• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python (1st ed.). Springer Texts in Statistics. Springer. Bishop, C. M. (2024). Deep Learning Foundations and Concepts. Springer.
• Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufmann Publishers.
• Provost, F., & Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media.
Prüfungsmodalitäten
Written exam (90min)