Data Science with R
I The Basics
1
Basic Installation
1.1
R and RStudio
1.2
LateX
1.3
Orientation
1.4
Libraries in R
1.5
Markdown
2
Vectors, Matrices & Other Grouped Data
2.1
Vectors
2.2
Matrices
2.3
Lists
2.4
Data Frames
2.5
Data From External Sources
II Some Basic Probability and Statistics
3
Learning About the Data
3.1
Qualitative Data Visualization
3.2
Quantatiative Data Visualization
3.3
Descriptive Statistics
3.3.1
Measures of Central Tendency
3.3.2
Measures of Spread
4
Probability Distributions
4.1
Definition of Probability Distributions
4.2
Types of Probability Distributions
4.2.1
Binomial Distribution
4.2.2
Poisson Distribution
4.2.3
Geometric Distribution
4.2.4
The Normal Distribution
4.2.5
Exponential Distribution
4.3
Uniform Distribution
5
Hypothesis Testing
III Creating Codebooks
6
Missing Data
6.1
Dealing with Missing Data
6.2
Types of Missing Data
7
Data Dictionaries
7.1
Purpose of a Data Dictionary
7.2
Components of a Data Dictionary
7.3
Benefits of Using a Data Dictionary
7.4
Examples
IV The Tidyverse
8
dplyr
9
ggplot2
V Unsupervised Learning
10
Unsupervised Learning
11
K-Means Clustering
12
Hierarchical Clustering
Basic Statistical Computing and Data Science Using R
Chapter 5
Hypothesis Testing