R Language

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Course Content

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