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Course Features
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Language: English
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40 hours
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Study Level: Intermediate
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Certificate of Completion

Data Science with R Course Agenda

LESSON ONE – Introduction to Business Analytics

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science Overview
  • Conclusion
  • Knowledge Check

LESSON TWO – 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
  • Knowledge Check

LESSON THREE – Data Structures

  • Overview
  • Identifying Data Structures
  • Demo: Identifying Data Structures
  • Assigning Values to Data Structures
  • Data Manipulation
  • Demo: Assigning values and applying functions
  • Conclusion
  • Knowledge Check

LESSON FOUR – Data Visualization

  • Overview
  • Introduction to Data Visualization
  • Data Visualization using Graphics in R
  • ggplot2
  • File Formats of Graphic Outputs
  • Conclusion
  • Knowledge Check

LESSON FIVE – Statistics for Data Science – I

  • Overview
  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels
  • Conclusion
  • Knowledge Check

LESSON SIX – Statistics for Data Science – II

  • Overview
  • Hypothesis Test
  • Parametric Test
  • Non-Parametric Test
  • Hypothesis Tests about Population Means
  • Hypothesis Tests about Population Variance
  • Hypothesis Tests about Population Proportions
  • Conclusion
  • Knowledge Check

LESSON SEVEN – 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
  • Knowledge Check

LESSON EIGHT – Classification

  • Overview
  • Classification and its Types
  • Logistic Regression
  • Support Vector Machines
  • Demo: 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
  • Knowledge Check

LESSON NINE – Clustering

  • Overview
  • Introduction to Clustering
  • Clustering Example
  • Clustering Methods: Prototype Based Clustering
  • Demo: K-means Clustering
  • Clustering Methods: Hierarchical Clustering
  • Demo: Hierarchical Clustering
  • Clustering Methods: DBSCAN
  • Conclusion
  • Knowledge Check

LESSON TEN – Association

  • Overview
  • Association Rule
  • Apriori Algorithm
  • Demo: Apriori Algorithm
  • Conclusion
  • Knowledge Check.

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