The Learn By Example: Statistics and Data Science in R course is taught by a former Googler with a Stanford education and a top analyst with degrees from IIT and IIM. Both of them have extensive real-world expertise in analytics, e-commerce, and quant trading. The course provides a thorough introduction to Data Science, Statistics, and R using examples from everyday life.

It begins by explaining fundamental ideas like the mean, median, etc., and finally covers every facet of a career in analytics or data science, from processing and analyzing raw data to presenting the findings visually. Examples from real life, case studies, and R source code are used to illustrate each idea. The examples span a wide range of subjects, from A/B testing in the context of an Internet company to the capital asset pricing model in the context of quantitative finance. The course is usually available for INR 1,499 on Udemy but you can click now to get the Learn By Example: Statistics and Data Science in R Course for INR 499.

Who all can opt for this course?

  • MBA graduates or business professionals seeking a move into a position that requires a strong mathematical background.
  • Engineers who wish to comprehend fundamental statistics and establish the groundwork for a Data Science career.
  • Experts in analytics who have primarily worked in descriptive analytics but want to transition to become modelers or data scientists.
  • Anyone who wishes to learn how to utilize R for statistical analysis but has previously worked with Excel-based tools.

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 1,49970% off
Duration09 hours
Student Enrollment4,866 students
InstructorLoony Corn
Topics CoveredDescriptive statistics, case studies, R, vectors, arrays, matrices, factors, etc.
Course LevelBeginner
Total Student Reviews376

Learning Outcomes

  • Harness R and its packages to read, process, and visualize data.
  • Learn about linear regression and use it to design models.
  • Learn about the intricacies of all the different data structures in R.
  • In order to get over the limitations of LINEST() in Excel, use linear regression in R.
  • Draw conclusions from the data and back them up with statistical tests.
  • Make a short analysis of some data using descriptive statistics, then report the findings.

Course Content

S.No.Module (Duration)Topics
1.Introduction (20 minutes)You, This course and us
Top Down vs Bottoms up: The Google vs McKinsey way of looking at data
R and RStudio installed
2.The 10 second answer: Descriptive Statistics (49 minutes)Descriptive Statistics: Mean, Median, Mode
Our first foray into R: Frequency Distributions
Draw your first plot: A Histogram
Computing Mean, Median, and Mode in R
What is IQR (Inter-quartile Range)
Box and Whisker Plots
The Standard Deviation
Computing IQR and Standard Deviation in R
3.Inferential Statistics (45 minutes)Drawing inferences from data
Random Variables are ubiquitous
The Normal Probability Distribution
Sampling is like fishing
Sample Statistics and Sampling Distributions
4.Case studies in Inferential Statistics (01 hour 07 minutes)Case Study 1: Football Players (Estimating Population Mean from a Sample)
Case Study 2: Election Polling (Estimating Population Proportion from a Sample)
Case Study 3: A Medical Study (Hypothesis Test for the Population Mean)
Case Study 4: Employee Behavior (Hypothesis Test for the Population Proportion)
Case Study 5: A/B Testing (Comparing the means of two populations)
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
5.Diving into R (45 minutes)Harnessing the power of R
Assigning Variables
Printing an output
Numbers are of type numeric
Characters and Dates
6.Vectors (01 hour 02 minutes)Data Structures are the building blocks of R
Creating a Vector
The Mode of a Vector
Vectors are Atomic
Doing something with each element of a Vector
Aggregating Vectors
Operations between vectors of the same length
Operations between vectors of different length
Generating Sequences
Using conditions with Vectors
Find the lengths of multiple strings using Vectors
Generate a complex sequence (using recycling)
Vector Indexing (using numbers)
Vector Indexing (using conditions)
Vector Indexing (using names)
7.Arrays (30 minutes)Creating an Array
Indexing an Array
Operations between 2 Arrays
Operations between an Array and a Vector
Outer Products
8.Matrices (16 minutes)A Matrix is a 2-Dimensional Array
Creating a Matrix
Matrix Multiplication
Merging Matrices
Solving a set of linear equations
9.Factors (17 minutes)What is a factor?
Find the distinct values in a dataset (using factors)
Replace the levels of a factor
Aggregate factors with table()
Aggregate factors with tapply()
10.Lists and Data Frames (30 minutes)Introducing Lists
Introducing Data Frames
Reading Data from files
Indexing a Data Frame
Aggregating and Sorting a Data Frame
Merging Data Frames
11.Regression quantifies relationships between variables (35 minutes)Introducing Regression
What is Linear Regression?
A Regression Case Study: The Capital Asset Pricing Model (CAPM)
12.Linear Regression in Excel (26 minutes)Linear Regression in Excel: Preparing the data
Linear Regression in Excel: Using LINEST()
13.Linear Regression in R (01 hour 04 minutes)Linear Regression in R: Preparing the data
Linear Regression in R: lm() and summary()
Multiple Linear Regression
Adding Categorical Variables to a linear model
Robust Regression in R: rlm()
Parsing Regression Diagnostic Plots
14.Data Visualization in R (34 minutes)Data Visualization
The plot() function in R
Control color palettes with RColorbrewer
Drawing bar plots
Drawing a heatmap
Drawing a Scatterplot Matrix
Plot a line chart with ggplot2

Resources Required

No prerequisites. As part of the course, the instructor will demonstrate how to install R and RStudio and use them for the majority of the examples.

Featured Review

Pushpendu Talukder (5/5): The way Trainers are explaining the concept of statistics using R is awesome. I had my hate-love relationship with statistics during college days and work life. This is the first time in my life that I am really understanding the significance of statistics and can relate to real-world situations.


  • Jerry Bernardi (5/5): For me, it was a great review of statistics along with an introduction to R.
  • Jerry Bernardi (5/5): I feel that the course material is great and the teaching style is very enjoyable.
  • Micah Shull (5/5): Excellent course! Lots of valuable information is presented in an easy-to-digest format.
  • Robert H. Woodman (4/5): This is a good refresher course for statistics, and it is proceeding at a good pace.


  • Vlad Mitroi (2/5): They mix up formulas all the time, making things rather confusing instead of ..well, teaching.
  • Vlad Mitroi (2/5): All in all, I hope i’ll get a refund due to the (lack of) quality of this course.

About the Author

The course is offered by Loonycorn who are a team of ex-Googlers, and Stanford graduates. With a 4.2 instructor rating and 26,244 reviews on Udemy, they offer 67 courses and have taught 154,973 students so far.

  • Loonycorn is a team of two people – Janani Ravi and Vitthal Srinivasan.
  • Together, they have worked in tech for years in the Bay Area, New York, Singapore, and Bangalore.
  • They also attended Stanford University and were accepted into IIM Ahmedabad.
  • Janani worked for Google for seven years in New York and Singapore.
  • She attended Stanford and has previously worked for Flipkart and Microsoft.
  • Vitthal studied at Stanford and worked with Google (Singapore), Flipkart, Credit Suisse, and INSEAD.

Comparison Table

ParametersLearn By Example: Statistics and Data Science in RR Programming Hands-on Specialization for Data Science (Lv1)From 0 to 1: Spark for Data Science with Python
OffersINR 455 (INR 1,499) 70% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration9 hours11 hours8.5 hours
Rating3.7/54.3 /54.7 /5
Student Enrollments4,86621,4488,070
InstructorsLoony CornIrfan ElahiLoony Corn
Register HereApply Now!Apply Now!Apply Now!

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