R Programming for Statistics and Data Science course walks through the essential concepts of R programming required for the Statistics and Data Science field. The course is an all-in-one package where students can learn coding in R from scratch.

The course is suitable for students who want to learn R, statistics, and data science to brush up their skill set as well as for aspiring data scientists who want to implement R in data analysis. The course is usually available for INR 3,499 on Udemy but you can click now to get 87% off and get the R Programming for Statistics and Data Science 2023 Course for INR 449.

Who all can opt for this course?

  • Aspiring Data Scientists
  • Programmers
  • Those with an Interest in Data Analysis and Statistics
  • Everyone who wants to get Coding Knowledge and Practise using it

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration06 hours
Student Enrollment24,928 students
Instructor365 Careers https://www.linkedin.com/in/365careers
Topics CoveredIntroduction to Statistics, Essentials of R Programming, Data Manipulation & Analysis, etc.
Course LevelBeginner
Total Student Reviews4,308

Learning Outcomes

  • Learn the fundamentals of R programming
  • Work with the loops, functions, and conditional statements provided by R
  • Create custom functions in R
  • Bring your data into R and out again
  • Utilize the ecosystem of packages in the Tidyverse to manipulate data
  • R data exploration
  • Learn about the ggplot2 package and visual syntax
  • Visualize data to plot various sorts of data and derive conclusions
  • Best practices regarding when and how to transform data
  • Data by index, slice, and subset
  • Learn the foundations of statistics and use them in real-world situations
  • R hypothesis testing
  • Recognize regression analysis in R and perform it
  • Make use of dummy variables
  • Develop the ability to make informed decisions
  • Enjoy dissecting data from Star Wars and Pokemon as well as more important data sets

Course Content

S.No.Module (Duration)Topics
1.Introduction (03 minutes)Ten Things You Will Learn in This Course
2.Getting started (18 minutes)Intro
Downloading and installing R & RStudio
Quick guide to the RStudio user interface
RStudio’s GUI
Changing the appearance in RStudio
Installing packages in R and using the library
3.The building blocks of R (35 minutes)Creating an object in R
Exercise 1 Creating an object in R
Data types in R – Integers and doubles
Data types in R – Characters and logicals
Objects and Data Types
Exercise 2 Data types in R
Coercion rules in R
Exercise 3 Coercion rules in R
Functions in R
Exercise 4 Using functions in R
Functions and arguments
Exercise 5 The arguments of a function
Building a function in R (basics)
Objects and Functions
Exercise 6 Building a function in R
Using the script vs. using the console
4.Vectors and vector operations (29 minutes)Intro
Introduction to vectors
Vector recycling
Exercise 7 Vector recycling
Naming a vector in R
Exercise 8 Vector attributes – names
Introduction to vectors
Getting help with R
Getting Help with R
Slicing and indexing a vector in R
Extracting elements from a vector
Exercise 9 Indexing and slicing a vector
Changing the dimensions of an object in R
Exercise 10 Vector attributes – dimensions
5.Matrices (49 minutes)Creating a matrix in R
Faster code: creating a matrix in a single line of code
Creating a matrix
Exercise 11 Creating a matrix in R
Do matrices recycle?
Indexing an element from a matrix
Slicing a matrix in R
Exercise 12 Indexing and slicing a matrix
Matrix arithmetic
Exercise 13 Matrix arithmetic
Matrix operations in R
Matrix operations
Exercise 14 Matrix operations
Categorical data
Creating a factor in R
Factors in R
Exercise 15 Creating a factor in R
Lists in R
Exercise: Lists in R
Completed 33% of the course
6.Fundamentals of programming with R (45 minutes)Relational operators in R
Logical operators in R
Vectors and logicals operators
Relational and Logical operators in R
Exercise Logical operators
If, else, else if statements in R
Exercise If, else, else if statements in R
If, else, else if statements – Keep-In-Mind’s
For loops in R
Exercise: For Loops in R
While loops in R
Exercise: While loops in R
Repeat loops in R
Loops in R
Building a function in R 2.0
Building a function in R 2.0 – Scoping
Exercise Scoping
Completed 50% of the course
7.Data frames (37 minutes)Intro
Creating a data frame in R
Exercise 16 Creating a data frame in R
The Tidyverse package
Data import in R
Importing a CSV in R
Data export in R
Exercise 17 Importing and exporting data in R
Creating data frames
Getting a sense of your data frame
Indexing and slicing a data frame in R
Data frame operations
Extending a data frame in R
Exercise 18 Data frame operations
Dealing with missing data in R
8.Manipulating data (26 minutes)Intro
Data transformation with R – the Dplyr package – Part I
Data transformation with R – the Dplyr package – Part II
Sampling data with the Dplyr package
Using the pipe operator in R
Manipulating data
Exercise 19 Data transformation with Dplyr
Tidying data in R – gather() and separate()
Tidying data in R – unite() and spread()
Tidying data
Exercise 20 Data tidying with Tidyr
9.Visualizing data (44 minutes)Intro
Intro to data visualization
Intro to ggplot2
Variables: revisited
Building a histogram with ggplot2
Exercise 21 Building a histogram with ggplot2
Building a bar chart with ggplot2
Exercise 22 Building a bar chart with ggplot2
Building a box and whiskers plot with ggplot2
Exercise 23 Building a box plot with ggplot2
Building a scatterplot with ggplot2
Exercise 24 Building a scatterplot with ggplot2
10.Exploratory data analysis (26 minutes)Population vs. sample
Mean, median, mode
Exercise 25 Determining Skewness
Variance, standard deviation, and coefficient of variability
Covariance and correlation
Exercise 26 Practical example with real estate data
11.Hypothesis Testing (56 minutes)Distributions
Standard Error and Confidence Intervals
Hypothesis testing
Type I and Type II errors
Test for the mean-population variance known
Exercise: Test for the mean-population variance known
The P-value
Test for the mean – Population variance unknown
Exercise: Test for the mean-population variance unknown
Comparing two means – Dependent samples
Exercise: Comparing two means – Dependent samples
Comparing two means – Independent samples
12.Linear Regression Analysis (26 minutes)The linear regression model
Correlation vs regression
Geometrical representation
First regression in R
How to interpret the regression table
Exercise: Doing a regression in R
Decomposition of variability: SST, SSR, SSE
Completed 100% of the course

Resources Required

  • R Studio – Instructors will walk you through the installation process

Featured Review

R K (5/5): Simona is an excellent instructor. Course is short but a good introduction to R . You never get bored & simona made fun as well as informative. The best part is she responds to every question you have. Great course! Thanks.


  • Sadanand Shirke (5/5): This is the best course before we get in to R.
  • Abril Izquierdo (5/5): In my case I am self taught in R, but never took any course or lesson, so I just wanted to perfect the basics, and it did!
  • Amitav Gupta (5/5): Best pick to start off the journey of data Analysis 3.Good Explanation of the syntaxes used
  • Rachel Reed (5/5): Since I have no programming experience, this course has been excellent.


  • Rachel Liu (2/5): This is at least the 11th courses I am taking in Udemy.
  • Andre Munoz (2/5): The problem here is that the code examples do the bare minimum to illustrate a technique but are meaningless (random number sets) or worse, impractical (such as the role playing card game) for any real world business situation.
  • Esmail Afsah (1/5): this is an awful course – I have done a 2 other courses on R on Udemy, either better – especially the one by Kirill Eremenko

About the Author

The course is instructed by 365 Careers who are creating opportunities for Data Science and Finance students. With a 4.6 instructor rating and 6,18,572 reviews on Udemy, they offer 91 courses and have taught 2,143,657 students so far.

  • On Udemy, 365 Careers is the top-selling provider of courses in business, finance, and data science.
  • In 210 different countries, more than 2,000,000 students have taken the company’s courses.
  • People who have finished 365 Careers training now work at renowned companies like Apple, PayPal, and Citibank.
  • On Udemy right now, 365 focuses on the following subjects: Finance, Data Science, Entrepreneurship, Office Productivity, Business, and Blockchain.
  • The courses offered by 365 Careers are the ideal place to start whether you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur.

Comparison Table

ParametersR Programming for Statistics and Data Science 2023Logistic Regression in PythonSupport Vector Machines in Python: SVM Concepts & Code
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration6.5 hours7.5 hours6.5 hours
Rating4.6 /54.6 /54.5 /5
Student Enrollments24,92698,70284,824
Instructors365 CareersStart-Tech AcademyStart-Tech Academy
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating