R has a steep learning curve . If you are one of those who have heard the Buzz around Data Science and has thought of Learning R ,but still not been able to do much , start going through "R" learning materials on this website. Install "R" , "R Studio" and just spend some time . It takes a while before you start getting connected and start using R for the Advantage that it provides .

Contact at TJT@TechnicalJockey.com , if you are looking for an Economical Instructor Based Online Training For R Programming that can get you connected to the Beautiful "R" world on a consistent basis before you start Flying !!!

R Programming Training Course Content

Course 1: R Programming Workshop

Pre-requisite : None

This Course is for an Analytics Beginner with Little or No Programming Experience where in we go through all the R programming concepts step by step giving proper time and complete Hands on till the concept sticks .

This course creates the Foundation and is very important if you want to start your Analytical Journey and wants to do Machine learning / Statistical Computing using R in the near future.

All the time spent here really pays off as based on our personal experience in Data Science , just doing a Google Search and pasting the code in R Console can only give temporary happiness !! 

  1. Introduction to R Programming
  2. R as a Calculator 
  3. Understanding Vectors through use case
  4. Understanding Matrices through use case
  5. Understanding Factors through use case
  6. Understanding Data Frames through use case
  7. Understanding Lists through use cases
  8. Understanding Operators through use case
  9. Understanding Conditional Statements through use case
  10. Understanding Functions through use case
  11. Understanding Packages through use case
  12. Start Working on  Dates & Time in R
  13. Setting up the Platform for Data Science through R Programming
  14. Step By Step Learning of How to Load External Data into R 
  15. Using skills learned above for Data Pre Processing through R
  16. Data Cleansing Hands On 
  17. Using dplyr package for Data Manipulation & Exploration 
  18. Inner Join / Left Join / Right Join /  Full Join / Semi Join - Joins In R using dplyr 
  19. Data Visualization Overview using ggplot2

Course 2 : Machine Learning With R Programming

Pre-requisite : Course 1 (R Programming)

  1. Supervised vs Unsupervised ML
  2. Classification Vs Regression
  3. Linear Regression - Simple , Multiple in R
  4. Logistic Regression - Simple, Multiple in R
  5. Decision Trees
  6. Random Forest
  7. SVM 
  8. Naive bayes
  9. K Nearest Neighbors ( KNN )
  10. Hierarchial Clustering
  11. K Means Clustering 
  12. Market Basket Analysis

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