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Statistics and Data Science in R Course
Introduction
You, This course and Us (2:32)
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data (12:58)
R and RStudio installed (5:10)
The 10 second answer : Descriptive Statistics
Descriptive Statistics : Mean, Median, Mode (10:07)
Our first foray into R : Frequency Distributions (6:07)
Draw your first plot : A Histogram (3:11)
Computing Mean, Median, Mode in R (2:21)
What is IQR (Inter-quartile Range)? (8:08)
Box and Whisker Plots (3:11)
The Standard Deviation (10:24)
Computing IQR and Standard Deviation in R (6:06)
Inferential Statistics
Drawing inferences from data (3:25)
Random Variables are ubiquitous (16:54)
The Normal Probability Distribution (9:31)
Sampling is like fishing (6:14)
Sample Statistics and Sampling Distributions (9:25)
Case studies in Inferential Statistics
Case Study 1 : Football Players (Estimating Population Mean from a Sample) (6:45)
Case Study 2 : Election Polling (Estimating Population Proportion from a Sample) (7:50)
Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean) (13:53)
Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion) (9:49)
Case Study 5: A/B Testing (Comparing the means of two populations) (17:18)
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) (11:50)
Diving into R
Harnessing the power of R (7:26)
Assigning Variables (8:48)
Printing an output (13:03)
Numbers are of type numeric (5:25)
Characters and Dates (7:30)
Logicals (3:24)
Vectors
Data Structures are the building blocks of R (8:24)
Creating a Vector (2:23)
The Mode of a Vector (4:18)
Vectors are Atomic (2:24)
Doing something with each element of a Vector (3:09)
Aggregating Vectors (1:28)
Operations between vectors of the same length (5:39)
Operations between vectors of different length (5:30)
Generating Sequences (6:25)
Using conditions with Vectors (2:04)
Find the lengths of multiple strings using Vectors (2:22)
Generate a complex sequence (using recycling) (2:49)
Vector Indexing (using numbers) (6:56)
Vector Indexing (using conditions) (6:18)
Vector Indexing (using names) (2:27)
Arrays
Creating an Array (11:36)
Indexing an Array (7:38)
Operations between 2 Arrays (2:09)
Operations between an Array and a Vector (2:45)
Outer Products (6:23)
Matrices
A Matrix is a 2-Dimensional Array (7:59)
Creating a Matrix (2:00)
Matrix Multiplication (2:49)
Merging Matrices (2:06)
Solving a set of linear equations (2:06)
Factors
What is a factor? (6:48)
Find the distinct values in a dataset (using factors) (1:28)
Replace the levels of a factor (2:18)
Aggregate factors with table() (1:39)
Aggregate factors with tapply() (5:07)
Lists and Data Frames
Introducing Lists (5:11)
Introducing Data Frames (4:28)
Reading Data from files (4:52)
Indexing a Data Frame (5:38)
Aggregating and Sorting a Data Frame (6:28)
Merging Data Frames (3:29)
Regression quantifies relationships between variables
Introducing Regression (12:22)
What is Linear Regression? (16:06)
A Regression Case Study : The Capital Asset Pricing Model (CAPM) (6:34)
Linear Regression in Excel
Linear Regression in Excel : Preparing the data (9:53)
Linear Regression in Excel : Using LINEST() (16:48)
Linear Regression in R
Linear Regression in R : Preparing the data (13:05)
Linear Regression in R : lm() and summary() (16:04)
Multiple Linear Regression (12:16)
Adding Categorical Variables to a linear model (7:44)
Robust Regression in R : rlm() (3:14)
Parsing Regression Diagnostic Plots (12:10)
Data Visualization in R
Data Visualization (6:23)
The plot() function in R (3:42)
Control color palettes with RColorbrewer (4:15)
Drawing barplots (5:25)
Drawing a heatmap (2:52)
Drawing a Scatterplot Matrix (3:41)
Plot a line chart with ggplot2 (8:19)
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
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