Data Science

Content

Objective

This course aims to introduce students to the data mining process. The main objectives of the course include understanding and applying data preparation and preprocessing techniques, various data mining algorithms, and the tools used to evaluate their results. The course focuses on standard approaches related to association rule mining, supervised classification, and unsupervised classification (clustering). Basic statistical knowledge is required to understand mining algorithms and quality evaluation tools. In this way, the course aims to enable students to produce practical solutions in the field of data analysis.

Course Content

- data pre-processing
- supervised classification
- clustering
- complex data mining
- results validation and quality assessment

Course Learning Outcomes

1. Data preparation
2. Theoretical and practical knowledge of standard data mining algorithms
3. Standard assessment tools

Teaching and Learning Methods

theoretical & practical class
assignments

References

1. Data Mining - Practical Machine Learning Tools, 2nd edition, Ian H. Witten & Eibe Frank, Morgan Kaufmann, 2005.
2. Neural Networks - A Comprehensive Foundation, 2nd edition, Simon Haykin, Pearson/Prentice Hall,1999.
3. Data Mining: Concepts and Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann, 2000.
4. Applied Statistics and Probabilities for Engineers, 4th edition, D.C. Montgomery & G.C. Runger, John Willey & sons, 2006.
5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, T. Hastie, R. Tibshirani & J. Friedman, Springer, 2009.

Weekly Contents

Assessment System

Relation of Proficiency

ECTS