Data Science - Part 1

1.      Motivations for data mining. Examples of application domains. Methodology of knowledge discovery in     databases (KDD) and data mining (DM). Formulation of main problems of data mining.
2.      Understanding data: preparation and exploration. Sampling.
3.      Basics of classification. Concepts of training and prediction. Decision trees.
4.      Models and algorithms for classification: k-NN, naïve-Bayes. Measuring quality and comparison of classification models.
5.      Introduction to the WEKA data mining software. Classification with WEKA.
6.      More models and algorithms for classification: neural networks, linear separation methods, support vector machine (SVM).
7.      Feature selection: filter and wrapper methods. Midterm test.
8.      Basics of cluster analysis. Type of variables, measuring similarity and distances. Partitioning clustering algorithms, k-means, k-medoids.
9.      Hierarchical clustering algorithms. Density based clustering, DBSCAN, OPTICS. Cluster analysis with WEKA.
10. Introduction to frequent itemset mining. Applications for finding association rules.
11. Level-wise algorithms, APRIORI. Partitioning and Toivonen algorithms.
12. Pattern growth methods, FP-growth. Constraints handling.
13. Hierarchical and general association rules. Pattern mining with WEKA.
14. Sequental and subgraph patterns. Final test.