Programming using R

R programming language is popular among statisticians and data miners for developing statistical software and data analysis. R Training has picked up a lot of popularity in recent years and there is a lot of demand for R Language Training.

Objectives

• Master the use of the R and RStudio interactive environment
• Expand R by installing R packages
• Read Structured Data into R from various sources
• Understand the different data structures in R
• Understand how to create and manipulate dates in R
• Use the tidyverse collection of packages to manipulate dataframes
• Write user-defined R functions
• Write Loop constructs in R
• Use the apply family of functions to iterate functions across data
• Expand iteration and programming through the Purrr package
• Reshape data from long to wide and back to support different analyses
• Perform merge operations with R
• Understand split-apply-combine (group-wise operations) in R
• Identify and deal with missing data
• Manipulate strings in R
• Understand basic regular expressions in R
• Understand base R graphics

Target Audience

• Ideal for graduates with 0 – 3 years of experience & degrees in B. Tech, B.E and B.Sc. IT Or Any Computer-Related program

Prerequisites

• Knowledge of statistics theory in mathematics.
• You should have solid understanding of statistics in mathematics.
• Understanding of various type of graphs for data representation.
• Prior knowledge of any programming.

Duration

• If you have experience in any programming language, it takes 7 days to learn R programming spending at least 3 hours a day. If you are a beginner, it takes 3 weeks to learn R programming. In the second week, learn concepts like how to create, append, subset datasets, lists, join.

Course Outline

Module 1: Introduction to Data Analytics

Objectives:

• This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
• ou can also learn how R can play an important role in solving complex analytical problems.
• This module tells you what is R and how it is used by the giants like Google, Facebook etc.
• Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics:

• Business Analytics, Data, Information
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R
• Perform basic operations in R using command line
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Module 2: Introduction to R programming

Objectives:

• This module starts from the basics of R programming like datatypes and functions.
• In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
• You will also learn how to apply the ‘join’ function in SQL.

Topics:

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Module 3: Data Manipulation in R

Objectives:

• In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.

Topics:

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

Module 4: Data Import techniques in R

Objectives:

• This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
• This module teaches you various data importing techniques in R.

Topics:

• Reading Data
• Writing Data
• Basic SQL queries in R
• Web Scraping

Module 5: Exploratory Data Analysis

Objectives:

• In this module, you will learn that exploratory data analysis is an important step in the analysis.
• EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics:

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing graphs

Module 6: Overview of Machine Learning techniques

Objectives:

• This module touches the base Statistics, Machine learning techniques used in the Industry and will cover case studies.

Topics:

• Standard deviation
• Outlier
• Linear regression
• Multiple regression
• Logistic regressions
• Correlation

Module 7: Project Work

• 1 Real-time project