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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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