Lesson 00 – Course Introduction

• Course Introduction

• Lesson 01 – Introduction to Business Analytics
  • 1.001 Overview
  • 1.002 Business Decisions and Analytics
  • 1.003 Types of Business Analytics
  • 1.004 Applications of Business Analytics
  • 1.005 Data Science Overview
  • 1.006 Conclusion
  • Knowledge Check
• Lesson 02 – Introduction to R Programming
  • 2.001 Overview
  • 2.002 Importance of R
  • 2.003 Data Types and Variables in R
  • 2.004 Operators in R
  • 2.005 Conditional Statements in R
  • 2.006 Loops in R
  • 2.007 R script
  • 2.008 Functions in R
  • 2.009 Conclusion
  • Knowledge Check
• Lesson 03 – Data Structures
  • 3.001 Overview
  • 3.002 Identifying Data Structures
  • 3.003 Demo Identifying Data Structures
  • 3.004 Assigning Values to Data Structures
  • 3.005 Data Manipulation
  • 3.006 Demo Assigning values and applying functions
  • 3.007 Conclusion
  • Knowledge Check
• Lesson 04 – Data Visualization
  • 4.001 Overview
  • 4.002 Introduction to Data Visualization
  • 4.003 Data Visualization using Graphics in R
  • 4.004 ggplot2
  • 4.005 File Formats of Graphic Outputs
  • 4.006 Conclusion
  • Knowledge Check
• Lesson 05 – Statistics for Data Science-I
  • 5.001 Overview
  • 5.002 Introduction to Hypothesis
  • 5.003 Types of Hypothesis
  • 5.004 Data Sampling
  • 5.005 Confidence and Significance Levels
  • 5.006 Conclusion
  • Knowledge Check
• Lesson 06 – Statistics for Data Science-II
  • 6.001 Overview
  • 6.002 Hypothesis Test
  • 6.003 Parametric Test
  • 6.004 Non-Parametric Test
  • 6.005 Hypothesis Tests about Population Means
  • 6.006 Hypothesis Tests about Population Variance
  • 6.007 Hypothesis Tests about Population Proportions
  • 6.008 Conclusion
  • Knowledge Check
• Lesson 07 – Regression Analysis
  • 7.001 Overview
  • 7.002 Introduction to Regression Analysis
  • 7.003 Types of Regression Analysis Models
  • 7.004 Linear Regression
  • 7.005 Demo Simple Linear Regression
  • 7.006 Non-Linear Regression
  • 7.007 Demo Regression Analysis with Multiple Variables
  • 7.008 Cross Validation
  • 7.009 Non-Linear to Linear Models
  • 7.010 Principal Component Analysis
  • 7.011 Factor Analysis
  • 7.012 Conclusion
  • Knowledge Check
• Lesson 08 – Classification
  • 8.001 Overview
  • 8.002 Classification and Its Types
  • 8.003 Logistic Regression
  • 8.004 Support Vector Machines
  • 8.005 Demo Support Vector Machines
  • 8.006 K-Nearest Neighbours
  • 8.007 Naive Bayes Classifier
  • 8.008 Demo Naive Bayes Classifier
  • 8.009 Decision Tree Classification
  • 8.010 Demo Decision Tree Classification
  • 8.011 Random Forest Classification
  • 8.012 Evaluating Classifier Models
  • 8.013 Demo K-Fold Cross Validation
  • 8.014 Conclusion
  • Knowledge Check
• Lesson 09 – Clustering
  • 9.001 Overview
  • 9.002 Introduction to Clustering
  • 9.003 Clustering Methods
  • 9.004 Demo K-means Clustering
  • 9.005 Demo Hierarchical Clustering
  • 9.006 Conclusion
  • Knowledge Check
• Lesson 10 – Association