Big Data and Data Science, Big Data Architecture, Small Data, What is Data, A short Taxonomy of Data Analytics, Examples of Data Use. Descriptive Statistics: Scale types, Descriptive Univariate Analysis, Univariate Frequencies, Univariate Data Visualization, Univariate Statistics-Common Univariate Probability Distributions.

Descriptive Multivariate Analysis: 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.

Clustering: Distance Measures, Clustering Validation, Clustering Technique: K-Means-Centroids and Distance Measures, DBSCAN. Frequent Pattern Mining: Frequent Itemsets, Association Rules, Behind Support and Confidence, Other type of Patterns- Sequential Patterns.

Regression: Predictive performance Estimation, Generalization, Model Validation, Finding the Parameters of the Model, Linear Regression, Empirical Error, the Bias-variance Trade-Off, Shrinkage Methods, Ridge Regression, Lasso Regression, Methods that use Linear Combinations of Attributes.

Classification: Binary Classification, Predictive performance Measures for Classification, Distance-based Learning Algorithms-K-nearest Neighbor Algorithms, case-based Reasoning, Probabilistic Classification Algorithms, Logistic Regression Algorithm, Naïve Bayes Algorithm, Search based Algorithm, Decision tree Induction Algorithm, Decision Trees for Regression.