Learn By Example: Statistics and Data Science in R [Video]

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Learn By Example: Statistics and Data Science in R [Video]

Loonycorn

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

ISBN 139781788996877
Course Length9 hours and 7 minutes

Video Description

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 analyzing and preparing raw data to visualizing 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.

Style and Approach

With no prerequisites to the course, this is the hands-on course for Statistics and Data Science.

Table of Contents

Introduction
You, This course and Us
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
R and RStudio installed
The 10 second answer: Descriptive Statistics
Descriptive Statistics: Mean, Median, Mode
Our first foray into R: Frequency Distributions
Draw your first plot: A Histogram
Computing Mean, Median, Mode in R
What is IQR (Inter-quartile Range)?
Box and Whisker Plots
The Standard Deviation
Computing IQR and Standard Deviation in R
Inferential Statistics
Drawing inferences from data
Random Variables are ubiquitous
The Normal Probability Distribution
Sampling is like fishing
Sample Statistics and Sampling Distributions
Case studies in Inferential Statistics
Case Study 1: Football Players (Estimating Population Mean from a Sample)
Case Study 2: Election Polling (Estimating Population Proportion from a Sample)
Case Study 3: A Medical Study (Hypothesis Test for the Population Mean)
Case Study 4: Employee Behaviour (Hypothesis Test for the Population Proportion)
Case Study 5: A/B Testing (Comparing the means of two populations)
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
Diving into R
Harnessing the power of R
Assigning Variables
Printing an output
Numbers are of type numeric
Characters and Dates
Logicals
Vectors
Data Structures are the building blocks of R
Creating a Vector
The Mode of a Vector
Vectors are Atomic
Doing something with each element of a Vector
Aggregating Vectors
Operations between vectors of the same length
Operations between vectors of different length
Generating Sequences
Using conditions with Vectors
Find the lengths of multiple strings using Vectors
Generate a complex sequence (using recycling)
Vector Indexing (using numbers)
Vector Indexing (using conditions)
Vector Indexing (using names)
Arrays
Creating an Array
Indexing an Array
Operations between 2 Arrays
Operations between an Array and a Vector
Outer Products
Matrices
A Matrix is a 2-Dimensional Array
Creating a Matrix
Matrix Multiplication
Merging Matrices
Solving a set of linear equations
Factors
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
Introducing Lists
Introducing Data Frames
Reading Data from files
Indexing a Data Frame
Aggregating and Sorting a Data Frame
Merging Data Frames
Regression quantifies relationships between variables
Introducing Regression
What is Linear Regression?
A Regression Case Study : The Capital Asset Pricing Model (CAPM)
Linear Regression in Excel
Linear Regression in Excel : Preparing the data
Linear Regression in Excel : Using LINEST()
Linear Regression in R
Linear Regression in R : Preparing the data
Linear Regression in R : lm() and summary()
Multiple Linear Regression
Adding Categorical Variables to a linear model
Robust Regression in R : rlm()
Parsing Regression Diagnostic Plots
Data Visualization in R
Data Visualization
The plot() function in R
Control color palettes with RColorbrewer
Drawing barplots
Drawing a heatmap
Drawing a Scatterplot Matrix
Plot a line chart with ggplot2

What You Will 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

Authors

Table of Contents

Introduction
You, This course and Us
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
R and RStudio installed
The 10 second answer: Descriptive Statistics
Descriptive Statistics: Mean, Median, Mode
Our first foray into R: Frequency Distributions
Draw your first plot: A Histogram
Computing Mean, Median, Mode in R
What is IQR (Inter-quartile Range)?
Box and Whisker Plots
The Standard Deviation
Computing IQR and Standard Deviation in R
Inferential Statistics
Drawing inferences from data
Random Variables are ubiquitous
The Normal Probability Distribution
Sampling is like fishing
Sample Statistics and Sampling Distributions
Case studies in Inferential Statistics
Case Study 1: Football Players (Estimating Population Mean from a Sample)
Case Study 2: Election Polling (Estimating Population Proportion from a Sample)
Case Study 3: A Medical Study (Hypothesis Test for the Population Mean)
Case Study 4: Employee Behaviour (Hypothesis Test for the Population Proportion)
Case Study 5: A/B Testing (Comparing the means of two populations)
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
Diving into R
Harnessing the power of R
Assigning Variables
Printing an output
Numbers are of type numeric
Characters and Dates
Logicals
Vectors
Data Structures are the building blocks of R
Creating a Vector
The Mode of a Vector
Vectors are Atomic
Doing something with each element of a Vector
Aggregating Vectors
Operations between vectors of the same length
Operations between vectors of different length
Generating Sequences
Using conditions with Vectors
Find the lengths of multiple strings using Vectors
Generate a complex sequence (using recycling)
Vector Indexing (using numbers)
Vector Indexing (using conditions)
Vector Indexing (using names)
Arrays
Creating an Array
Indexing an Array
Operations between 2 Arrays
Operations between an Array and a Vector
Outer Products
Matrices
A Matrix is a 2-Dimensional Array
Creating a Matrix
Matrix Multiplication
Merging Matrices
Solving a set of linear equations
Factors
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
Introducing Lists
Introducing Data Frames
Reading Data from files
Indexing a Data Frame
Aggregating and Sorting a Data Frame
Merging Data Frames
Regression quantifies relationships between variables
Introducing Regression
What is Linear Regression?
A Regression Case Study : The Capital Asset Pricing Model (CAPM)
Linear Regression in Excel
Linear Regression in Excel : Preparing the data
Linear Regression in Excel : Using LINEST()
Linear Regression in R
Linear Regression in R : Preparing the data
Linear Regression in R : lm() and summary()
Multiple Linear Regression
Adding Categorical Variables to a linear model
Robust Regression in R : rlm()
Parsing Regression Diagnostic Plots
Data Visualization in R
Data Visualization
The plot() function in R
Control color palettes with RColorbrewer
Drawing barplots
Drawing a heatmap
Drawing a Scatterplot Matrix
Plot a line chart with ggplot2

Video Details

ISBN 139781788996877
Course Length9 hours and 7 minutes
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From 2 reviews

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