Learn By Example: Statistics and Data Science in R

Learn By Example: Statistics and Data Science in R

$39 $15

A gentle yet thorough introduction to Data Science, Statistics and R using real life examples

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Product Description

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.

Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What’s Covered:

Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2

Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Mail us about anything – anything! – and we will always reply 🙂

What am I going to get from this course?

  • Harness R and R packages to read, process and visualize data
  • Understand linear regression and use it confidently to build models
  • Understand the intricacies of all the different data structures in R
  • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
  • Draw inferences from data and support them using tests of significance
  • Use descriptive statistics to perform a quick study of some data and present results

What is the target audience?

  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
  • Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
  • Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
  • Yep! Folks who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis


  • M1 - You This course and Us
  • M1 - Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
  • M1 - R and RStudio installed
  • M2 - Descriptive Statistics : Mean Median Mode
  • M2 - Our first foray into R : Frequency Distributions
  • M2 - Draw your first plot : A Histogram
  • M2 - Computing Mean Median Mode in R
  • M2 - What is IQR (Inter-quartile Range)?
  • M2 - Box and Whisker Plots
  • M2 - The Standard Deviation
  • M2 - Computing IQR and Standard Deviation in R
  • M3 - Drawing inferences from data
  • M3 - Random Variables are ubiquitous
  • M3 - The Normal Probability Distribution
  • M3 - Sampling is like fishing
  • M3 - Sample Statistics and Sampling Distributions
  • M4 - Case Study 1 : Football Players (Estimating Population Mean from a Sample)
  • M4 - Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
  • M4 - Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
  • M4 - Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
  • M4 - Case Study 5: A/B Testing (Comparing the means of two populations)
  • M4 - Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
  • M5 - Harnessing the power of R
  • M5 - Assigning Variables
  • M5 - Printing an output
  • M5 - Numbers are of type numeric
  • M5 - Characters and Dates
  • M5 - Logicals
  • M6 - Data Structures are the building blocks of R
  • M6 - Creating a Vector
  • M6 - The Mode of a Vector
  • M6 - Vectors are Atomic
  • M6 - Doing something with each element of a Vector
  • M6 - Aggregating Vectors
  • M6 - Operations between vectors of the same length
  • M6 - Operations between vectors of different length
  • M6 - Generating Sequences
  • M6 - Using conditions with Vectors
  • M6 - Find the lengths of multiple strings using Vectors
  • M6 - Generate a complex sequence (using recycling)
  • M6 - Vector Indexing (using numbers)
  • M6 - Vector Indexing (using conditions)
  • M6 - Vector Indexing (using names)
  • M7 - Creating an Array
  • M7 - Indexing an Array
  • M7 - Operations between 2 Arrays
  • M7 - Operations between an Array and a Vector
  • M7 - Outer Products
  • M8 - A Matrix is a 2-Dimensional Array
  • M8 - Creating a Matrix
  • M8 - Matrix Multiplication
  • M8 - Merging Matrices
  • M8 - Solving a set of linear equations
  • M9 - What is a factor?
  • M9 - Find the distinct values in a dataset (using factors)
  • M9 - Replace the levels of a factor
  • M9 - Aggregate factors with table()
  • M9 - Aggregate factors with tapply()
  • M10 - Introducing Lists
  • M10 - Introducing Data Frames
  • M10 - Reading Data from files
  • M10 - Indexing a Data Frame
  • M10 - Aggregating and Sorting a Data Frame
  • M10 - Merging Data Frames
  • M11 - Introducing Regression
  • M11 - What is Linear Regression?
  • M11 - A Regression Case Study : The Capital Asset Pricing Model (CAPM)
  • M12 - Linear Regression in Excel : Preparing the data
  • M12 - Linear Regression in Excel : Using LINEST()
  • M13 - Linear Regression in R : Preparing the data
  • M13 - Linear Regression in R : lm() and summary()
  • M13 - Multiple Linear Regression
  • M13 - Adding Categorical Variables to a linear model
  • M13 - Robust Regression in R : rlm()
  • M13 - Parsing Regression Diagnostic Plots
  • M14 - Data Visualization
  • M14 - The plot() function in R
  • M14 - Control color palettes with Rcolorbrewer
  • M14 - Drawing barplots
  • M14 - Drawing a heatmap
  • M14 - Drawing a Scatterplot Matrix
  • M14 - Plot a line chart with ggplot2

Course Requirements

No prerequisites : We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of excel is assumed.
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