Inhalt
[ 926BUSIDAM13 ] Module Data Mining
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| Workload |
Mode of examination |
Education level |
Study areas |
Responsible person |
Coordinating university |
| 6 ECTS |
Accumulative module examination |
M1 - Master's programme 1. year |
Business Informatics |
Christoph Schütz |
Johannes Kepler University Linz |
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| Detailed information |
| Original study plan |
Master's programme Business Informatics 2023W |
| Objectives |
Students will be able to apply data mining methods to integrated and cleaned data sets of an organization, but also to process logs and unstructured data such as text data, in such a way that potentially new knowledge can be gained through pattern recognition. They are familiar with the phases of data mining, important application areas (problem types) and current developments in data, web as well as process mining. The students are familiar with the use of data mining tools.
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| Subject |
Overall process of data mining (KDD - Knowledge Discovery in Data); techniques of data mining: clustering, classification, association rules, time series analysis; process mining; text mining, incl. sentiment analysis and opinion mining; simple visualization of results; applications of data mining, e.g. recommender system; tools for data mining; recent developments; case studies and practical applications, especially web mining and predictive/prescriptive analytics.
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| Further information |
Lecture and exercise can be combined to one course.
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| Subordinated subjects, modules and lectures |
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