Big Data and Data Science -Big Data Architectures - Small Data -What is Data? - A Short Taxonomy of Data Analytics - Examples of Data Use - A Project on Data Analytics -Descriptive Statistics -Scale Types -Descriptive Univariate Analysis - Univariate Data Visualization - Univariate Statistics - Common Univariate Probability Distributions - Descriptive Bivariate Analysis -Two Quantitative Attributes -Two Qualitative Attributes, at Least one of them Nominal -Two Ordinal Attributes.

Multivariate Frequencies -Multivariate Data Visualization -Multivariate Statistics -Infographics and Word Clouds - Data Quality and Preprocessing -Data Quality -Converting to a Different Scale Type -Converting to a Different Scale- Data Transformation- Dimensionality Reduction- Attribute Aggregation -Attribute Selection.

Clustering- Distance Measures -Clustering Validation -Clustering Techniques -K- means – DBSCAN-Agglomerative Hierarchical Clustering Technique - Frequent Pattern Mining - Frequent Item sets -Association Rules -Behind Support and Confidence-Other Types of Pattern.

Regression -Predictive Performance Estimation -Finding the Parameters of the Model - Linear Regression-The Bias-variance Trade-off-Shrinkage Methods-Methods that use Linear Combinations of Attributes -Technique and Model Selection -Classification -Binary Classification -Predictive Performance Measures for Classification -Distance-based Learning Algorithms - Probabilistic Classification Algorithms.

Additional Predictive Methods-Search-based Algorithms-Decision Tree Induction Algorithms-Decision Trees for Regression-Optimization-based Algorithms -Artificial Neural Networks -Support Vector Machines- Advanced Predictive Topics-Ensemble Learning -Algorithm Bias -Non-binary Classification Tasks -Advanced Data Preparation Techniques for Prediction - Imbalanced Data Classification-For Incomplete Target Labeling- Description and Prediction with Supervised Interpretable Techniques.