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R – PROGRAMMING (35 Hrs.)

R Language

  • Getting R
  • Downloading R
  • R Version
  • 32-bit versus 64-bit
  • Installing
  • The R Environment
  • Command Line Interface
  • RStudio
  • R Packages
  • Installing Packages
  • Loading Packages
  • Basics of R
  • Basic Math
  • Variables
  • Data Types
  • Vectors
  • Calling Functions
  • Function Documentation
  • Missing Data
  • Advanced Data Structures
  • Data.frames
  • Lists
  • Matrices
  •  Arrays
  • Factors
  • Reading Data into R
  • Reading CSVs
  • Excel Data
  • Clipboard
  • Control Statements
  • if and else
  • switch
  • ifelse
  • Compound Tests
  • Loops
  • for Loops
  • while Loops
  • Controlling Loops
  • Group Manipulation
  • Apply Family
  • aggregate
  • Data Reshaping
  • cbind and rbind
  • Joins
  • Reshape2
  • String Theory
  • paste
  • sprintf
  • Extracting Text
  • Regular Expressions
  • Graphs with R and GGPlot2
  • Basic and Interactive Plots
  • Dendrograms
  • Pie Chart and Its Alternatives
  • Adding the Third Dimension
  • Visualizing Continuous Data
  • Basic Statistics
  • Summary Statistics
  • Correlation and Covariance
  • T-Tests
  • ANOVA
  • Probability Distributions
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Statistical Methods & Machine Learning Algorithms
  • Descriptive statistics and Inferential statistics– R Code

1.1 Linear Regression – Theory
1.2 Linear Regression – R Code
2.1 Logistic Regression – Theory
2.2 Logistic Regression – R Code
3.1 Market Basket Analysis – Theory
3.1 Market Basket Analysis – R Code
4.1 Naive Bayes – Theory
4.1 Naive Bayes – R Code
5.1 Neural Network – Theory
5.1 Neural Network – R Code
6.1 Principal Component Analysis – Theory
6.2 Principal Component Analysis – R Code
7.1 Time Series Analysis – Theory
7.2 Time Series Analysis – R Code
8.1 Unsupervised learning: Clustering – Theory
8.2 Unsupervised learning: Clustering – R Code
9.1 Decision Trees – Theory
9.2 Decision Trees – R Code
10.1 K Nearest Neighbors (kNN) – Theory
10.2 K Nearest Neighbors (kNN) – R Code

  • Case Study
  • Resume preparation
  • Interview Questions/Tips
  • Approach to Interview/ How to follow up
  • Exclusively doubts clarification on every week end.
  • Guiding in Real time
Sample Description
Sample Description