Learn By Example: Statistics and Data Science in R

A gentle yet thorough introduction to Data Science, Statistics and R using real life examples
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  • Lectures 82
  • Length 9 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 4/2016 English

Course 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 RLine 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 are the 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.

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

Who 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

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Introduction

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


Q. How do companies make decisions? 

A. Using data

We talk about what it takes to go from data to making a decision from data. This sets the agenda for the rest of the course - each of the things on this journey is covered in the upcoming sections


Get setup with R and Rstudio. All the examples that follow in this course will have source code attached. Download and run them in Rstudio

Section 2: The 10 second answer : Descriptive Statistics

Bosses are impatient. They often want you to cut to the chase, and give them an answer that's ok, but in a short amount of time. Descriptive statistics are the first place to start - they are often the 10s answer to any question about the data. 


Computing a frequency distribution using R


A histogram is a good visual summary of your data. 


Computing the Mean, Median, Mode in R


The mean, median and mode are point estimates to represent your data. IQR is a measure that explains the spread of the data.


Visualize the IQR and outliers using box and whisker plots


The standard deviation measures the spread of a dataset, and it so happens, the standard deviation is actually very profound.

Computing IQR and Standard Deviation in R
Section 3: Inferential Statistics

Drawing inferences from data is key to being able to take decisions using data. There is a science to this, whose foundation is in random variables, probability distributions, and performing tests of statistical significance. 


Random variables are everywhere. Any data that you'll study is a random variable whose behaviour is determined by a probability distribution.


The Normal Distribution is arguably the most well-known and commonly seen probability distribution. It is characterized by its probability density function, mean and standard deviation.


Sampling is a little like fishing. Sampling is crucial to induction - drawing conclusions about something by looking at some evidence.


A sample is described by sample statistics like the sample mean. The sampling distribution is the probability distribution of sample means. 

Section 4: Case studies in Inferential Statistics

Find a point estimate for the average weight of all football players using a sample of football players in 1 college team.


Find a point estimate for the % of voters in favor of a candidate.


A test of significance is an important step in building support for your findings and inferences. Here is the first example of a test of significance - is the population mean equal to a given value? 


Perform a test of significance to check whether the population % is equal to a certain value


Perform a test of significance to compare 2 population means. The example used is A/B Testing - which is pretty widely used in internet companies to test out product features.


Perform a test of significance to compare two population proportions

Section 5: Diving into R

The next few sections dive deep into all the data processing, slicing and dicing ability that R provides. The wide variety of R packages available is one reason why R is popular among many data scientists. 


Let's start with the basics. What are variables and how do we assign variables in R? 


print(), show(), message(), cat() are different ways to print something to screen. 


Numbers in R are of type numeric. 


R has built-in datatypes for dates and timestamps. 


Logical is a datatype that is the result of conditional tests in R

Section 6: Vectors

The wide variety of built-in data structures are what makes R different from other standard programming languages. These include vectors, arrays, matrices, data frames and lists. 

Creating a Vector

The mode of a vector is the datatype of all its elements. 

Vectors are Atomic
Doing something with each element of a Vector

Finding the sum, product, or mean of a vector

Operations between vectors of the same length
Operations between vectors of different length

Generate sequences using the : operator, rep() and seq()

Using conditions with Vectors
Find the lengths of multiple strings using Vectors
Generate a complex sequence (using recycling)

Access elements based on their position in the vector.


Access elements based on whether they pass a conditional test. 


Assign names to the elements of a vector

Section 7: Arrays

Creating an array can be done by using a vector and then arranging it along dimensions.

Indexing an Array
Operations between 2 Arrays
Operations between an Array and a Vector

Outer products are complex operations that operate on every pair of elements from two arrays.

Section 8: Matrices

A Matrix is a 2 Dimensional array. But it has special meaning and can be interpreted in a bunch of different ways.

Creating a Matrix
Matrix Multiplication

rbind() and cbind() to merge matrices.

Solving a set of linear equations
Section 9: Factors

A factor is a special type of vector used to represent categorical variables

Find the distinct values in a dataset (using factors)
Replace the levels of a factor
Aggregate factors with table()
Aggregate factors with tapply()
Section 10: Lists and Data Frames

Lists are fundamentally different from vectors, arrays and matrices - which are all homogenous data structures.


Data Frames are how R stores data read from files and databases.

Reading Data from files
Indexing a Data Frame

Using the aggregate() and order() functions


Merge data frames based on one or more common columns

Section 11: Regression quantifies relationships between variables

Regression is the process of finding a model that describes the relationship between variables. 


Linear regression is the process of fitting a line or a linear model that best explains the relationship between 2 variables. Understand what residuals are, the ordinary least squares method and R-Squared


The Capital Asset Pricing Model describes a relationship between risk and return. Use it with regression to either find the risk or returns of a given stock. Regression is one of the ways to estimate the Beta in CAPM.

Section 12: Linear Regression in Excel

Find the Beta of Google by regressing Google returns against NASDAQ returns. We describe how to find, and prepare the data for fitting a linear model. 


LINEST() is a function in excel that fits a linear model for a given set of variables. However LINEST() has a bunch of issues, including its inability to deal with missing values.

Section 13: Linear Regression in R

Find the Beta of Google by regressing Google returns against NASDAQ returns. We describe how process data frames and prepare the data for fitting a linear model. 


lm() is used to build linear models in R. The results of lm() can be parsed using summary(). Building the linear model in R has a bunch of advantages over doing the same in Excel.


Build a linear model with multiple independent variables : Regress the returns of an oil stock against S&P 500 and the returns of an exchange traded oil fund. 


We describe how categorical variables can be built into a linear model, and how to do this in R specifically


rlm() helps you build Robust linear models that downweight the influence of outliers.


lm() returns a bunch of diagnostic plots that are used to validate the assumptions underlying linear regression - Q-Q plots, Scale-location and Cook's distance plots

Section 14: Data Visualization in R

Data Visualization gives you the power to effectively get your point across and to deeply understand your data.

The plot() function in R
Control color palettes with RColorbrewer
Drawing barplots
Drawing a heatmap
Drawing a Scatterplot Matrix

ggplot2 is a pretty cool R package for complex 2D graphics. Plot the time series of 4 different stocks in the same graph. 

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Instructor Biography

Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)

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