Data Mining

Aims of the course

- To introduce students with basic concepts of big data and analytics, characteristics and specifics compared to traditional data analytics.
- To introduce students with approaches to data mining and text mining
- Introduce students with current data mining methods and tools
- Introduce students with the importance of and approaches to data preparation and model evaluation.

Course syllabus

1. Big Data Analytics Technologies
2. Basic Data Mining Concepts
3. Data Mining Tasks
4. Data Mining Process
5. Methods: association rules, kNN, clustering, decision trees, random forest, gradient boosted decision trees, support vector machines
6. Data Preparation: transformation, cleansing, reduction
7. Evaluation: train and test set, cross-validation
8. Text Mining: process, methods, sentiment analysis
9. Web Scraping

Course director(s)

  • Office Hours
  • Wednesday at 12:00 in RZ-404
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