Course curriculum

  • 1

    Introduction to Business Analytics

    • Overview

    • Business Decisions and Analytics

    • Types of Business Analytics

    • Applications of Business Analytics

    • Data Science Overview

    • Conclusion
  • 2

    Introduction to R Programming

    • Overview

    • Importance of R

    • Data Types and Variables In R

    • Operators in R

    • Conditional Statements in R

    • Loops in R

    • R Script

    • Functions in R

    • Conclusion
  • 3

    Data Structures

    • Overview

    • Identifying Data Structures

    • Demo Identifying Data Structures

    • Assigning Value to Data Structures

    • Data Manipulation

    • Demo Assigning Values and Applying Functions

    • Conclusion
  • 4

    Data Visualization

    • Overview

    • Introduction to Data Visualization

    • Data Visualization Using Graphics in R

    • GGPlot 2

    • File Formats of Graphic Outputs

    • Conclusion
  • 5

    Statistics for Data Science- 1

    • Overview

    • Introduction to Hypothesis

    • Types of Hypothesis

    • Data Sampling

    • Confidence and Significance Levels

    • Conclusion
  • 6

    Statistics for Data Science- 2

    • Overview

    • Hypothesis Test

    • Parametric Test

    • Non-Parametric Test

    • Hypothesis test about Population

    • Hypothesis about Population Variance

    • Hypothesis about Population Proportions

    • Conclusion
  • 7

    Regression Analysis

    • Overview

    • Introduction to Regression Analysis

    • Types of Regression Analysis Models

    • Linear Regression

    • Demo Simple Linear Regression

    • Non-Linear Regression

    • Demo Regression Analysis with Multiple Variables

    • Cross Validation

    • Non-Linear to Linear models

    • Principal Component Analysis

    • Factor Analysis

    • Conclusion
  • 8

    Classification

    • Overview

    • Classification and its types

    • Logistic Regression

    • Support Vector Machines

    • K-Nearest Neighbours

    • Naive Bayes Classifier

    • Demo Naive Bayes Classifier

    • Decision Tree Classification

    • Demo Decision Tree Classification

    • Random Forest Classification

    • Evaluating Classifier Models

    • Demo K-Fold Cross Validation

    • Conclusion
  • 9

    Clustering

    • Overview

    • Introduction to Clustering

    • Clustering Methods

    • Demo K-Means Clustering

    • Demo Hierarchical Clustering

    • Conclusion
  • 10

    Association

    • Overview

    • Association Rule

    • Apriori Algorithm

    • Demo Apriori Algorithm

    • Conclusion