Data Science and Applications

Content

Objective

This class aims at introducing the data mining process to students. This includes the description of data preparation and preprocessing, of various data mining algorithms and of the tools available to assess their results. The class focuses on standard approaches regarding association rules mining, supervised classification and unsupervised classification (clustering). Basic statistical knowledge is necessary to understand the mining algorithms and the quality assessment tools.

Course Content

W1: Introduction, overview
W2: Descriptive Statistics
W3: Data Preprocessing
W4: Inferential Statistics and its preprocessing tools
W5: Code Application 1
W6: Regression
W7: Classification1
W8: Classification2
W9: Clustering1, 2
W10: Code Application 2
W11: Project Presentations

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

• Data Mining - Practical Machine Learning Tools, 2nd edition, Ian H. Witten & Eibe Frank, Morgan Kaufmann, 2005.
• Neural Networks - A Comprehensive Foundation, 2nd edition, Simon Haykin, Pearson/Prentice Hall,1999.
• Data Mining: Concepts and Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann, 2000.
• Applied Statistics and Probabilities for Engineers, 4th edition, D.C. Montgomery & G.C. Runger, John Willey & sons, 2006.
• 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