Big Data and Data Science, A Project on Data Analytics - A Little History on Methodologies for Data Analytics, KDD Process, CRISP-DM Methodology; Data Analytics- Types, Tools, and Applications;

Descriptive Statistics - Scale Types, Descriptive Univariate Analysis, Descriptive Bivariate Analysis; Descriptive Multivariate Analysis - Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics, Infographics and Word Clouds; Data Quality - Missing Values, Redundant Data, Inconsistent Data, Noisy Data, Outliers.

Distance Measures - Differences between Values of Common Attribute Types, Distance Measures for Objects with Quantitative Attributes, Distance Measures for Non-conventional Attributes; Clustering Validation, Clustering Techniques - K-means, Centroids and Distance Measures, DBSCAN.

Binary Classification - Predictive Performance Measures for Classification; Distance-based Learning Algorithms - K-nearest Neighbor Algorithms, Case-based Reasoning; Probabilistic Classification Algorithms - Logistic Regression Algorithm, Naive Bayes Algorithm.

Regression and its types; DA Applications for Text, Web and social media - Working with Texts, Recommender Systems, Social Network Analysis