DSC 385: Syllabus

Course title and instructor

Title: DSC 385 Data Exploration, Visualization, and Foundations of Unsupervised Learning

Instructor: Claus O. Wilke
GitHub: clauswilke

Purpose and contents of the class

In this class, students will learn how to visualize data sets and how to reason about and communicate with data visualizations. Students will also learn how to assess data quality and provenance, how to compile analyses and visualizations into reports, and how to make the reports reproducible. A substantial component of this class will be dedicated to learning how to program in R.

What you will learn:

Data visualization
R programming
Reproducibility
Data quality and relevance
Data ethics and provenance
Dimension reduction
Clustering

Prerequisites

Students are expected to have basic knowledge of statistics. Prior experience with the programming language R is beneficial but not strictly required.

Textbook

This class draws heavily from materials presented in the following book:

Claus O. Wilke. Fundamentals of Data Visualization. O’Reilly Media, 2019.

Additionally, we will also make use of the following books:

Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen. ggplot2: Elegant Graphics for Data Analysis, 3rd ed. Springer, to appear.
Kieran Healy. Data Visualization: A Practical Introduction. Princeton University Press, 2018.

All these books are freely available online and you do not need to purchase a physical copy of either book to succeed in this class.

Topics covered

Class	Topic	Coding concepts covered
1.	Introduction, reproducible workflows	RStudio setup online, R Markdown
2.	Aesthetic mappings	ggplot2 quickstart
3.	Telling a story
4.	Visualizing amounts	`geom_col()`, `geom_point()`, position adjustments
5.	Coordinate systems and axes	coords and position scales
6.	Visualizing distributions 1	stats, `geom_density()`, `geom_histogram()`
7.	Visualizing distributions 2	violin plots, sina plots, ridgeline plots
8.	Color scales	color and fill scales
9.	Data wrangling 1	`mutate()`, `filter()`, `arrange()`
10.	Data wrangling 2	`group_by()`, `summarize()`, `count()`
11.	Visualizing proportions	bar charts, pie charts
12.	Getting to know your data 1: Data provenance
13.	Getting to know your data 2: Data quality and relevance	handling missing data, `is.na()`, `case_when()`
14.	Getting things into the right order	`fct_reorder()`, `fct_lump()`
15.	Figure design	ggplot themes
16.	Color spaces, color vision deficiency	colorspace package
17.	Functions and functional programming	`map()`, `nest()`, purrr package
18.	Visualizing trends	`geom_smooth()`
19.	Working with models	`lm`, `cor.test`, broom package
20.	Visualizing uncertainty	frequency framing, error bars, ggdist package
21.	Dimension reduction 1	PCA
22.	Dimension reduction 2	kernel PCA, t-SNE, UMAP
23.	Clustering 1	k-means clustering
24.	Clustering 2	hierarchical clustering
25.	Data ethics
26.	Visualizing geospatial data	`geom_sf()`, `coord_sf()`
27.	Redundant coding, text annotations	ggrepel package
28.	Interactive plots	ggiraph package
29.	Over-plotting	jittering, 2d histograms, contour plots
30.	Compound figures	patchwork package

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.

Syllabus