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R Tutorial: Statistics and Data Science in R

This Statistics with R tutorial created by a Stanford alumni team teaches how to use R for Data Science. Topics like vectors, dataframes, regression, and data visualization are taught with real life examples in R.

09h:04m
Lifetime access
50 learners
Introduction to the Course

This online Statistics with R tutorial begins with an introduction to the basic concepts of Statistics like mean, median, and mode, and eventually covers every aspect of an analytics or data science career, right from the analysis and preparation of raw data to visualising the findings. The course is an introduction to Data Science and Statistics using the R programming language. Every concept is explained using real time examples, case studies, and source code in R. The examples used cover a broad range, from the Capital Asset Pricing Model to A/B testing in the context of an e-commerce company.

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Course Objectives

What will you gain from this course?

  • The ability to harness R and R packages to read, analyse and visualise data
  • Knowledge about the intricacies of the various data structures in R
  • An understanding of linear regression and how to use it to build models; for example,to resolve the challenges of LINEST() in Excel
  • Know how to use descriptive Statistics to conduct a quick study on data and present results

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Prerequisites and Target Audience

The course begins with the basics. You will need to install R and RStudio as part of the course, and have basic knowledge of Excel. Anyone planning to start a data science career, who wants to add on the power of R for statistical analysis can take up this course.
This course is specifically designed for:

  • Engineers who seek to understand basic statistics and form a solid foundation for a career in Data Science
  • Business professionals and MBA graduates want to switch to a heavily quantitative role
  • Analytics professionals who have a background in working with descriptive analytics and want to move to being data scientists and modelers
  • Someone who has worked with Excel and now wants to learn how to use R for statistical analysis

Course Plan
Certificate of completion

2. The 10 second answer : Descriptive Statistics
8 videos
Descriptive Statistics: Mean, Median, Mode 10:07

Our first foray into R: Frequency Distributions 06:06

Draw your first plot: A Histogram 03:11

Computing Mean, Median, Mode in R 02:21

What is IQR (Inter-quartile Range)? 08:08

Box and Whisker Plots 03:11

The Standard Deviation 10:24

Computing IQR and Standard Deviation in R 06:06
3. Inferential Statistics
5 videos
Drawing inferences from data 03:25

Random Variables are ubiquitous 16:54

The Normal Probability Distribution 09:31

Sampling is like fishing 06:14

Sample Statistics and Sampling Distributions 09:25
4. Case studies in Inferential Statistics
6 videos
Case Study 1: Football Players (Estimating Population Mean from a Sample) 06:49

Case Study 2: Election Polling (Estimating Population Proportion from a Sample) 07:50

Case Study 3: A Medical Study (Hypothesis Test for the Population Mean) 13:53

Case Study 4: Employee Behavior (Hypothesis Test for the Population Proportion) 09:49

Case Study 5: A/B Testing (Comparing the means of two populations) 17:18

Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) 11:50
5. Diving into R
6 videos
Harnessing the power of R 07:26

Assigning Variables 08:48

Printing an output 13:03

Numbers are of type numeric 05:25

Characters and Dates 07:30

Logicals 03:24
6. Vectors
15 videos
Data Structures are the building blocks of R 08:24

Creating a Vector 02:23

The Mode of a Vector 04:18

Vectors are Atomic 02:24

Doing something with each element of a Vector 03:09

Aggregating Vectors 01:28

Operations between vectors of the same length 05:39

Operations between vectors of different length 05:30

Generating Sequences 06:25

Using conditions with Vectors 02:04

Find the lengths of multiple strings using Vectors 02:22

Generate a complex sequence (using recycling) 02:49

Vector Indexing (using numbers) 06:56

Vector Indexing (using conditions) 06:18

Vector Indexing (using names) 02:27
7. Arrays
5 videos
Creating an Array 11:36

Indexing an Array 07:38

Operations between 2 Arrays 02:09

Operations between an Array and a Vector 02:45

Outer Products 06:23
8. Matrices
5 videos
A Matrix is a 2-Dimensional Array 07:58

Creating a Matrix 02:00

Matrix Multiplication 02:48

Merging Matrices 02:06

Solving a set of linear equations 02:06
9. Factors
5 videos
What is a factor? 06:48

Find the distinct values in a dataset (using factors) 01:28

Replace the levels of a factor 02:18

Aggregate factors with table() 01:39

Aggregate factors with tapply() 05:07
10. Lists and Data Frames
6 videos
Introducing Lists 05:11

Introducing Data Frames 04:28

Reading Data from files 04:52

Indexing a Data Frame 05:38

Aggregating and Sorting a Data Frame 06:28

Merging Data Frames 03:29
11. Regression quantifies relationships between variables
3 videos
Introducing Regression 12:21

What is Linear Regression? 16:06

A Regression Case Study: The Capital Asset Pricing Model (CAPM) 06:35
12. Linear Regression in Excel
2 videos
Linear Regression in Excel: Preparing the data 09:53

Linear Regression in Excel: Using LINEST() 16:48
13. Linear Regression in R
6 videos
Linear Regression in R: Preparing the data 13:05

Linear Regression in R: lm() and summary() 16:04

Multiple Linear Regression 12:16

Adding Categorical Variables to a linear model 07:44

Robust Regression in R: rlm() 03:14

Parsing Regression Diagnostic Plots 12:10
14. Data Visualization in R
7 videos
Data Visualization 06:23

The plot() function in R 03:42

Control color palettes with RColorbrewer 04:15

Drawing barplots 05:25

Drawing a heatmap 02:52

Drawing a Scatterplot Matrix 03:41

Plot a line chart with ggplot2 08:19

Meet the Author


Loonycorn
4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
  • More from Loonycorn
    Ratings and Reviews     4.7/5

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