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

• Master the use of the R and RStudio interactive environment

• Expand R by installing R packages

• Explore and understand how to use the R documentation

• Read Structured Data into R from various sources

• Understand the different data types in R

• 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

• Use control statements

• 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

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.

• You 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