Data Science
29
Data Visualization
Home
1
Introduction
2
Schedule
R Programming
3
Installation
4
Introduction to R
5
Getting Started
6
IDEs
7
R packages
8
Basic operations
9
Data Types & Vectors
10
Data Structures
11
Indexing
12
Factors
13
Base Data I/O
14
Read and Write Excel Data
15
Vectorized Operations
16
Control Flow
17
Summarizing Data
18
Aggregate
19
Writing Functions
20
Function Scoping
21
The Apply Family
22
Base Graphics
Data Science
23
Terminal
24
Version Control with Git and GitHub
25
Demo Dataset
26
Probability
27
Statistics
28
Hypothesis Testing
29
Data Visualization
30
Introduction to Machine Learning
31
Clustering
32
Decomposition and Dimensionality Reduction
33
Supervised Learning
34
AI Assistants for Data Science
35
References
Table of contents
29.1
Learning objectives
29.2
From question to plot
29.3
Distributions
29.4
Relationships and group comparisons
29.5
Labels, scales, and color
29.6
Missingness and unusual observations
29.7
Demo: Visualization with rtemis
29.8
Hands-on practice
29.9
Checkpoint
29.10
Further study
Data Science
29
Data Visualization
29
Data Visualization
29.1
Learning objectives
Choose a display that matches the variables and question.
Explore distributions, relationships, and group differences.
Describe patterns and limitations without overstating what a plot shows.
29.2
From question to plot
29.3
Distributions
29.4
Relationships and group comparisons
29.5
Labels, scales, and color
29.6
Missingness and unusual observations
29.7
Demo: Visualization with rtemis
29.8
Hands-on practice
29.9
Checkpoint
29.10
Further study
28
Hypothesis Testing
30
Introduction to Machine Learning