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Become a Masters in
Data Science With R

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Join more than 1500+ students that have changed their future.
In just 3 months you can change your career and become a Data Analyst.
experienced industry practitioners.
Become a Masters in
Data Science With R

Become a Masters in
Data Science With R

Tutor of data science offers data science courses in Bangalore Marathahalli.

The only Data Science training program where you get in-depth knowledge of all the modules of Data Science. Become a master in Machine Learning, Python, R language, NLP, Statistics, Tableau, data exploration, data visualization, predictive analytics and descriptive analytics techniques. With this data science course, you’ll get hands-on Experience by implementing various real-life and industry-based projects in the domains of healthcare, retail, insurance, and many more.

What You Learn?

Data Science with R Course Agenda

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science Overview
  • Conclusion
  • Knowledge Check
  • 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
  • Overview
  • Identifying Data Structures
  • Demo: Identifying Data Structures
  • Assigning Values to Data Structures
  • Data Manipulation
  • Demo: Assigning values and applying functions
  • Conclusion
  • Knowledge Check
  • Overview
  • Introduction to Data Visualization
  • Data Visualization using Graphics in R
  • ggplot2
  • File Formats of Graphic Outputs
  • Conclusion
  • Knowledge Check
  • Overview
  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels
  • Conclusion
  • Knowledge Check
  • 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
  • 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
  • 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
  • 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
  • Overview
  • Association Rule
  • Apriori Algorithm
  • Demo: Apriori Algorithm
  • Conclusion
  • Knowledge Check
  • What Our Students Say

    Information

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