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)
2,095 students enrolled
Created by Loony Corn
Last updated 12/2016
English
Current price: \$10 Original price: \$50 Discount: 80% off
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30-Day Money-Back Guarantee
Includes:
• 9 hours on-demand video
• 132 Supplemental Resources
• 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
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.
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

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.

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
09:07:16
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Introduction
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
05:10
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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
10:07

Computing a frequency distribution using R

Our first foray into R : Frequency Distributions
06:06

A histogram is a good visual summary of your data.

Draw your first plot : A Histogram
03:11

Computing the Mean, Median, Mode in R

Computing Mean, Median, Mode in R
02:21

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)?
08:08

Visualize the IQR and outliers using box and whisker plots

Box and Whisker Plots
03:11

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

The Standard Deviation
10:24

Computing IQR and Standard Deviation in R
06:06
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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
03:25

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
16:54

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
09:31

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

Sampling is like fishing
06:14

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
09:25
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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)
06:45

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

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

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)
13:53

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)
09:49

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
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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
07:26

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

Assigning Variables
08:47

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

Printing an output
13:03

Numbers in R are of type numeric.

Numbers are of type numeric
05:24

R has built-in datatypes for dates and timestamps.

Characters and Dates
07:30

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

Logicals
03:24
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Vectors
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
08:24

Creating a Vector
02:22

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

The Mode of a Vector
04:18

Vectors are Atomic
02:24

Doing something with each element of a Vector
03:09

Finding the sum, product, or mean of a vector

Aggregating Vectors
01:28

Operations between vectors of the same length
05:39

Operations between vectors of different length
05:30

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

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

Access elements based on their position in the vector.

Vector Indexing (using numbers)
06:56

Access elements based on whether they pass a conditional test.

Vector Indexing (using conditions)
06:18

Assign names to the elements of a vector

Vector Indexing (using names)
02:27
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Arrays
5 Lectures 30:31

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

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

Outer Products
06:23
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Matrices
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
07:58

Creating a Matrix
02:00

Matrix Multiplication
02:48

rbind() and cbind() to merge matrices.

Merging Matrices
02:06

Preview 02:06
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Factors
5 Lectures 17:20

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

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
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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
05:11

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

Introducing Data Frames
04:28

04:52

Indexing a Data Frame
05:38

Using the aggregate() and order() functions

Aggregating and Sorting a Data Frame
06:28

Merge data frames based on one or more common columns

Merging Data Frames
03:29
4 More Sections