Data Science Process: Roles and stages in a data science project, working with files and databases, Exploring and managing data; Big Data- Types, Characteristics, Tools and Applications; Data Analytics- Types, Tools and Applications; Data and Relations: Data set - Data Scales - Set and Matrix Representations - Relations - Similarity Measures - Dissimilarity Measures - Sequence Relations – Sampling and Quantization.

Data preprocessing : Error Types - Error Handling - Filtering - Data Transformation - Data Merging; Data visualization: Diagrams - Principal Component Analysis - Multidimensional Scaling - Auto Associator - Histograms - Spectral Analysis.

Correlation: Linear Correlation - Causality - Chi-Square Tests; Regression: Linear Regression - Robust Regression - RBF Networks - Cross Validation - Feature Selection

Classification: Classification Criteria - Naive Bayes‘ Classifier -Rule Based Classification – Classification by Back Propagation - Support Vector Machine - Decision Trees - Lazy Learners – Model Evaluation and Selection-Techniques to improve Classification Accuracy.

Clustering: Cluster Partitions - Sequential - Prototype-Based - Fuzzy - Relational - Cluster Tendency Assessment - Cluster Validity - Self Organizing Maps; Case Study: Advertising on the Web.