Unit-1 Introduction to Data Science and AI & ML

• Data Science, AI & ML
• Use Cases in Business and Scope
• Scientific Method
• Modeling Concepts

 Unit 2- R Essentials (Tutorial) Programming

• Commands and Syntax
• Packages and Libraries
• Introduction to Data Types
• Importing and Exporting Data.
• Control structures and Functions

Unit-3 Statistical Analysis Initial Data Analysis

• Relationship between attributes: Covariance, Correlation Coefficient, Chi-Square
• Probability (Joint, marginal and conditional probabilities)
• Probability distributions (Continuous and Discrete)
• Density Functions and Cumulative functions

Unit-4 Data Pre-processing and Preparation

• Data Munging, Wrangling
• Plyr packages
• Cast/Melt

Unit-5: Data Quality and Transformation

• Data imputation
• Data Transformation (min-max, log transform, z-score transform
• Binning, Classing, and Standardization
• Outlier/Noise& Anomalies