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R Programming

Course Description

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

 R’s open-source nature offers companies the required boost. This is because this is a world that is focused on data and you have to shine in the competition. Data science is a great business priority as per Gartner Research. Now the reputation of R Training is going to see even more surge. Suppose you are keen to expand your horizons and you possess data evaluation skills, then R Language can be an amazing language

Introduction

  • What are Data Analysis, Data Analytics and Data Science?
  • Business Decisions
  • Case study of Walmart

Various analytics tools

  • Descriptive
  • Predictive
  • Web Analytics
  • Google Analytics
  • R and features
  • Evolution of R?
  • Big data Hadoop and R

Data Types

  • R & R Studio Installation
  • Scalar
  • Vectors
  • Matrix
  • List
  • Data frames
  • Factors
  • Handling date in R
  • Conversion of data types
  • Operators in R

Importing Data

  • CSV files
  • Database data (Oracle 11g)
  • XML files
  • JSON files
  • Reading & Writing PDF files
  • Reading & Writing JPEG files
  • Saving Data in R

Manipulating Data

  • Cbind, Rbind
  • Sorting
  • Aggregating
  • dplyr

Conditional Statements and Functions

  • If …else
  • For loop
  • While loop
  • Repeat loop
  • Apply()
  • sApply()
  • rApply()
  • tApply

Statistical Concepts

  • Descriptive Statistics
  • Inferential Statistics
  • Central Tendency (Mean,Mode,Median)
  • Hypothesis Testing
  • Probability
  • tTest
  • zTest
  • Chi Square test
  • Correlation
  • Covariance
  • Anova

Predictive Modelling

  • Linear Regression
  • Normal distribution
  • Density

Data Visualisation in R using GGPlot

  • Box Plot
  • Histograms
  • Scatter Plotter
  • Line chart
  • Bar Chart
  • Heat maps

Misc. functions and Data Visualization using Plotly

  • 3D-view
  • Geo Maps
  • Null Handling
  • Merge
  • Grep
  • Scan

Advance Topics in R

  • Text Mining
  • Exploratory Data Analysis
  • Machine Learning with R (concept)

Schedule

  • Week 1: Overview of R, R data types and objects, reading and writing data
  • Week 2: Control structures, functions, scoping rules, dates and times
  • Week 3: Loop functions, debugging tools
  • Week 4: Simulation, code profiling