Curriculum

1
Intro to R Programming
Learning Objectives:

Get an idea of what R is and why it is so popular among Data Scientists.

Topics Covered:

What is R?
Why is it in demand?
Hands-on: No hands-on

2
Installing and Loading Libraries
Learning Objectives:

In this module you will learn to install R and its components, install and load R libraries and learn about the frequently used libraries.

Topics Covered:

Installation of R – step by step
Installing Libraries
Getting to know important Libraries
Hands-on:

Know how to install R, R Studio and other libraries.

3
Data Structures in R
Learning Objectives:

Learn about data structures in R.

Topics Covered:

List
Vectors
Arrays
Matrices
Factors
String
Data Frames
Hands-on:

Write R Code to understand and implement R Data Structures.

4
Control & Loop Statements in R
Learning Objectives:

Learn all about loops and control statements in R.

Topics Covered:

For Loop
While Loop
Break Statement
Next Statements
Repeat Statement
if, if…else Statements
Switch Statement
Hands-on:

Write R Code to implement loop and control structures in R.

5
Functions in R
Learning Objectives:

Learn how to write custom functions, nested functions and functions with arguments.

Topics Covered:

Writing your own functions (UDF)
Calling R Functions
Nested Function Calls in R
Functions with Arguments
Calling R Functions by passing Arguments
Hands-on:

Write R Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required

6
Loop Functions in R
Learning Objectives:

Learn all about loop functions available in R which are efficient and can be written with a single command.

Topics Covered:

apply
lapply
sapply
mapply
Tapply
Hands-on:

Write R Code to implement various types of apply functions and understand their usage.

7
String Manipulation & Regular Expression in R
Learning Objectives:

Learn all about string manipulations and regular expressions. The functions can be extremely useful for text or unstructured data manipulations.

Topics Covered:

stringr()
grep() & grepl()
regexpr() & gregexpr()
regexec()
sub() & gsub()
Hands-on:

Write R Code for string manipulation and handle regular expression.

8
Working with Data in R
Learning Objectives:

Learn how to import data from various sources in R and how to write files from R. Also learn how to connect to various databases from R.

Topics Covered:

Reading data files in R
Reading data files from other Statistical Software
Writing files in R
Connecting to Databases from R
Data Manipulation & Analysis
Hands-on:

Write R Code to read and write data from/to R. Read data not only from CSV files but also using direct connection to various databases.

9
Querying, Filtering, and Summarizing
Learning Objectives:

Learn how to apply various data processing functions in R. These operations can be useful to describe data and perform certain operations on it. This will help you to take necessary steps for further analysis.

Topics Covered:

Pipe operator for data processing
Using the dplyr verbs
Using the customized function within the dplyr verbs
Using the select verb for data processing
Using the filter verb for data processing
Using the arrange verb for data processing
Using mutate for data processing
Using summarise to summarize a dataset
Hands-on:

Write R code to preprocess, to
summarize data and basic
visualization of the data

10
Basic Data Visualization
Learning Objectives:

Learn basic data visualization techniques to build charts using R.

Topics Covered:

Basic Data Visualization with standard libraries 
Hands-on:

Write R code to perform basic visualization of the data

11
Case Study
Learning Objectives:

Case Study to explore R.

Topics Covered:

Case Study: R Programming
Hands-on:

Case Study to explore R