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| Detailed information |
| Pre-requisites |
(*)Erwartete Vorkenntnisse: Grundlegende Programmierkenntnisse // Expected prior knowledge: Basic programming skills
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| Original study plan |
Master's programme Business Informatics 2025W |
| Learning Outcomes |
Competences |
| Students are able to carry out a systematic analysis of structured and unstructured company data and event logs of business processes with the help of data mining methods and tools, taking into account the current scientific state of the art. Students are able to use data mining methods and tools, taking into account the current scientific state of the art, to link different data sources and discover new knowledge about hidden patterns or special features.
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| Skills |
Knowledge |
- LO2: Students are able to prepare different data sources for analysis and relate them to each other (K3).
- LO3: Students master the application of the most important algorithms for supervised and unsupervised machine learning, know the advantages and disadvantages of these algorithms and can evaluate and compare concrete applications of these algorithms in data mining projects with regard to parameter selection (K4).
- LO4: Students are proficient in carrying out the phases of the data mining process (K3).
- LO5: Students can use data mining tools (K3).
- LO6: Students know important application areas (problem types) and current developments in data, web, text and process mining (K2).
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LO1: Overall process of data mining (knowledge discovery from data and CRISP-DM); data understanding and data preparation for analysis; techniques of data mining: clustering, classification, association rules; process mining; text mining and natural language processing, incl. sentiment analysis and language models; basics of neural networks and deep learning;
Graph and web mining; simple visualization of analysis results; applications of data mining, e.g. recommender systems; tools and programming languages for data mining; predictive and prescriptive analytics; trustworthy data mining
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| Criteria for evaluation |
Written exam (Midterm and Final Exam)
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| Methods |
The course content is taught using blended learning methods, in particular flipped classroom, with interactive elements to consolidate knowledge.
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| Language |
German/English |
| Study material |
Basic Literature:
- Han, J.; Pei, J.; Tong, H.: Data Mining: Concepts and Techniques. Morgan Kaufmann, current edition.
Supplemental Literature:
- Kelleher, J.D.; Mac Namee; B.; D'Arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics. MIT Press, current edition.
- Huntsinger, R.: Business Analytics. Cambridge University Press, current edition.
- McKinney, W.: Python for Data Analysis. O'Reilly, current edition.
- Van der Aalst, W.: Process Mining. Springer, current edition.
Other supplemental literature will be announced each semester.
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| Changing subject? |
No |
| Further information |
VL and UE Data Mining form an inseparable didactic unit. The learning outcomes described are achieved through the interaction of both.
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WBMDMV: VL Data Mining (2011S-2014S)
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