Data science using R

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.