Enterprise and IT Security (ENITS)

Do you aspire to a leadership position in the field of IT security? The advanced, comprehensive Master’s degree program Enterprise and IT Security (ENITS) of Offenburg University will open the doors for you and help you to achieve your goal.
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Modul manual

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Data Mining

Prerequisite

Requires basic knowledge of data bases, statistics and experience with a modern programming Language
Teaching methods Lecture/Lab
Learning target / Competences

- Introduction to data mining: overview, CRISP, data pre-processing, concepts of supervised and unsupervised learning, visual analytics
- Association rules
- Linear regression: simple linear regression, introduction to multiple linear regression
- Classification: logistic regression, decision trees, SVM
- Ensemble methods: bagging, random forests, boosting
- Clustering: K-means, K-medoids, Hierarchical clustering
- Evaluation and validation: cross-validation, assessing the statistical significance of data mining results
- Ethics and privacy
- Selection of advanced topics such as neural networks, outlier detection, relation to big data analysis
- In the lab, students apply data mining methods and algorithms to problem sets and develop data mining applications,
using tools such as R and RapidMiner
Duration 1
Hours per week 4.0
Overview
Classes 60
Individual / Group work: 120
Workload 180
ECTS 6.0
Requirements for awarding credit points

written exam, 60 Min. and report (Data Mining, Lab Data Mining)
Credits and grades

written exam, 60 min. (K60, Data Mining) and report (BE, Lab Data Mining)
Responsible person

Prof. Dr. Stephan Trahasch
Recommended semester 1
Frequency Every 2nd sem.
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