Learn By Example: Statistics and Data Science in R
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Learn By Example: Statistics and Data Science in R

A gentle yet thorough introduction to Data Science, Statistics and R using real life examples
4.3 (205 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
2,095 students enrolled
Created by Loony Corn
Last updated 12/2016
Current price: $10 Original price: $50 Discount: 80% off
1 day left at this price!
30-Day Money-Back Guarantee
  • 9 hours on-demand video
  • 132 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
  • 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.

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

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

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
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Curriculum For This Course
82 Lectures
3 Lectures 20:53

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

Preview 02:32

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

Preview 13:11

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

R and RStudio installed
The 10 second answer : Descriptive Statistics
8 Lectures 49:34

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. 

Descriptive Statistics : Mean, Median, Mode

Computing a frequency distribution using R

Our first foray into R : Frequency Distributions

A histogram is a good visual summary of your data. 

Draw your first plot : A Histogram

Computing the Mean, Median, Mode in R

Computing 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.

What is IQR (Inter-quartile Range)?

Visualize the IQR and outliers using box and whisker plots

Box and Whisker Plots

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

The Standard Deviation

Computing IQR and Standard Deviation in R
Inferential Statistics
5 Lectures 45:29

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. 

Drawing inferences from data

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

Random Variables are ubiquitous

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.

The Normal Probability Distribution

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

Sampling is like fishing

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

Sample Statistics and Sampling Distributions
Case studies in Inferential Statistics
6 Lectures 01:07:25

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

Case Study 1 : Football Players (Estimating Population Mean from a Sample)

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

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

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? 

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

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

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

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.

Preview 17:18

Perform a test of significance to compare two population proportions

Preview 11:50
Diving into R
6 Lectures 45:34

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. 

Harnessing the power of R

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

Assigning Variables

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

Printing an output

Numbers in R are of type numeric. 

Numbers are of type numeric

R has built-in datatypes for dates and timestamps. 

Characters and Dates

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

15 Lectures 01:02:35

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. 

Data Structures are the building blocks of R

Creating a Vector

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

The Mode of a Vector

Vectors are Atomic

Doing something with each element of a Vector

Finding the sum, product, or mean of a vector

Aggregating Vectors

Operations between vectors of the same length

Operations between vectors of different length

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

Generating Sequences

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.

Vector Indexing (using numbers)

Access elements based on whether they pass a conditional test. 

Vector Indexing (using conditions)

Assign names to the elements of a vector

Vector Indexing (using names)
5 Lectures 30:31

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

Creating an Array

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.

Outer Products
5 Lectures 16:58

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

A Matrix is a 2-Dimensional Array

Creating a Matrix

Matrix Multiplication

rbind() and cbind() to merge matrices.

Merging Matrices

5 Lectures 17:20

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

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()
Lists and Data Frames
6 Lectures 30:06

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

Introducing Lists

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

Introducing Data Frames

Reading Data from files

Indexing a Data Frame

Using the aggregate() and order() functions

Aggregating and Sorting a Data Frame

Merge data frames based on one or more common columns

Merging Data Frames
4 More Sections
About the Instructor
Loony Corn
4.3 Average rating
4,205 Reviews
31,237 Students
75 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  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

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 :-)