R Programming for Data Science Online Training Course

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R Programming for Data Science Online Training Course

R Programming Course Content
Introduction to R
R language for statistical programming, various features of R, introduction to RStudio, statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of RStudio like code editor, visualization and debugging tools and learn about R-bind

R Packages
R functions, code compilation and data in well-defined format called R Packages, R Package structure, package metadata and testing, CRAN (Comprehensive R Archive Network), vector creation and variables values assignment

Sorting DataFrame
R functionality, Rep function, generating repeats, sorting and generating factor levels, transpose and stack function

Matrices and Vectors
Introduction to matrix and vector in R, understanding various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions

Reading Data from External Files
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists and understanding how to read data from external files

Generating Plots
Generate plots in R, graphs, bar plots, line plots, histograms and components of a pie chart

Analysis of Variance (ANOVA)
K-Means Clustering
Association Rule Mining
Regression in R
Analyzing Relationship with Regression
Advanced Regression
Logistic Regression
Advanced Logistic Regression
Receiver Operating Characteristic (ROC)
Kolmogorov–Smirnov Chart
Database Connectivity with R
Integrating R with Hadoop
R Case Studies

R Programming Projects
What projects I will be working on this R Programming training?
Project 1

Domain: Restaurant Revenue Prediction

Data set: Sales

Project Description: This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real-time industry experience on handling multiple use cases and deriving the solutions. This project gives insights about feature engineering and selection.

Project 2

Domain: Data Analytics

Objective: The project is meant to predict the class of a flower using its petal’s dimensions.

Project 3

Domain: Finance

Objective: The project aims to find the most impacting factors in the preferences of pre-paid model and to identify which all are the variables highly correlated with impacting factors.

Project 4

Domain: Stock Market

Objective: This project focuses on Machine Learning by creating predictive data model to predict future stock prices.