Curriculum
14 Sections
82 Lessons
52 Weeks
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Introduction
3
2.1
You, This course and Us
3 Minutes
2.1
Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data
13 Minutes
2.1
R and RStudio installed
5 Minutes
The 10 second answer : Descriptive Statistics
8
3.1
Descriptive Statistics : Mean, Median, Mode
10 Minutes
3.1
Our first foray into R : Frequency Distributions
6 Minutes
3.1
Draw your first plot : A Histogram
3 Minutes
3.1
Computing Mean, Median, Mode in R
2 Minutes
3.1
What is IQR (Inter-quartile Range)?
8 Minutes
3.1
Box and Whisker Plots
3 Minutes
3.1
The Standard Deviation
10 Minutes
3.1
Computing IQR and Standard Deviation in R
6 Minutes
Inferential Statistics
5
4.1
Drawing inferences from data
3 Minutes
4.1
Random Variables are ubiquitous
17 Minutes
4.1
The Normal Probability Distribution
9 Minutes
4.1
Sampling is like fishing
6 Minutes
4.1
Sample Statistics and Sampling Distributions
9 Minutes
Case studies in Inferential Statistics
6
5.1
Case Study 1 : Football Players (Estimating Population Mean from a Sample)
7 Minutes
5.1
Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
8 Minutes
5.1
Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
14 Minutes
5.1
Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
10 Minutes
5.1
Case Study 5: A/B Testing (Comparing the means of two populations)
17 Minutes
5.1
Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
12 Minutes
Diving into R
6
6.1
Harnessing the power of R
7 Minutes
6.1
Assigning Variables
9 Minutes
6.1
Printing an output
13 Minutes
6.1
Numbers are of type numeric
5 Minutes
6.1
Characters and Dates
7 Minutes
6.1
Logicals
3 Minutes
Vectors
15
7.1
Data Structures are the building blocks of R
8 Minutes
7.1
Creating a Vector
2 Minutes
7.1
The Mode of a Vector
4 Minutes
7.1
Vectors are Atomic
2 Minutes
7.1
Doing something with each element of a Vector
3 Minutes
7.1
Aggregating Vectors
1 Minute
7.1
Operations between vectors of the same length
6 Minutes
7.1
Operations between vectors of different length
5 Minutes
7.1
Generating Sequences
6 Minutes
7.1
Using conditions with Vectors
2 Minutes
7.1
Find the lengths of multiple strings using Vectors
2 Minutes
7.1
Generate a complex sequence (using recycling)
3 Minutes
7.1
Vector Indexing (using numbers)
7 Minutes
7.1
Vector Indexing (using conditions)
6 Minutes
7.1
Vector Indexing (using names)
2 Minutes
Arrays
5
8.1
Creating an Array
12 Minutes
8.1
Indexing an Array
8 Minutes
8.1
Operations between 2 Arrays
2 Minutes
8.1
Operations between an Array and a Vector
3 Minutes
8.1
Outer Products
6 Minutes
Matrices
5
9.1
A Matrix is a 2-Dimensional Array
8 Minutes
9.1
Creating a Matrix
2 Minutes
9.1
Matrix Multiplication
3 Minutes
9.1
Merging Matrices
2 Minutes
9.1
Solving a set of linear equations
2 Minutes
Factors
5
10.1
What is a factor?
7 Minutes
10.1
Find the distinct values in a dataset (using factors)
1 Minute
10.1
Replace the levels of a factor
2 Minutes
10.1
Aggregate factors with table()
2 Minutes
10.1
Aggregate factors with tapply()
5 Minutes
Lists and Data Frames
6
11.1
Introducing Lists
5 Minutes
11.1
Introducing Data Frames
4 Minutes
11.1
Reading Data from files
5 Minutes
11.1
Indexing a Data Frame
6 Minutes
11.1
Aggregating and Sorting a Data Frame
6 Minutes
11.1
Merging Data Frames
3 Minutes
Regression quantifies relationships between variables
3
12.1
Introducing Regression
12 Minutes
12.1
What is Linear Regression?
16 Minutes
12.1
A Regression Case Study : The Capital Asset Pricing Model (CAPM)
7 Minutes
Linear Regression in Excel
2
13.1
Linear Regression in Excel : Preparing the data
10 Minutes
13.1
Linear Regression in Excel : Using LINEST()
17 Minutes
Linear Regression in R
6
14.1
Linear Regression in R : Preparing the data
13 Minutes
14.1
Linear Regression in R : lm() and summary()
16 Minutes
14.1
Multiple Linear Regression
12 Minutes
14.1
Adding Categorical Variables to a linear model
8 Minutes
14.1
Robust Regression in R : rlm()
3 Minutes
14.1
Parsing Regression Diagnostic Plots
12 Minutes
Data Visualization in R
7
15.1
Data Visualization
6 Minutes
15.1
The plot() function in R
4 Minutes
15.1
Control color palettes with RColorbrewer
4 Minutes
15.1
Drawing barplots
5 Minutes
15.1
Drawing a heatmap
3 Minutes
15.1
Drawing a Scatterplot Matrix
4 Minutes
15.1
Plot a line chart with ggplot2
8 Minutes
Learn By Example: Statistics and Data Science in R
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