This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.
week
dow
date
what
topic
prepare
slides
ae
ae_sa
hw
hw_sa
project
notes
1
Tu
Jan 23
Lec 1
Introduction to effective data communication
Th
Jan 25
Lec 2
The grammar of graphics
F
Jan 26
Meet the toolkit
hw-01
2
Tu
Jan 30
Lec 3
Deep dive: layers (I)
Th
Feb 1
Lec 4
Deep dive: layers (II)
F
Feb 2
Start on homework 02
hw-02
3
Tu
Feb 6
Lec 5
Statistical transformations, scales, and guides
Th
Feb 8
Lec 6
Coordinate systems and faceting
F
Feb 9
Develop project 1 proposals
4
Tu
Feb 13
Lec 7
Data wrangling (I)
Th
Feb 15
Lec 8
Data wrangling (II)
F
Feb 16
Peer review project 1 proposals
hw-03
5
Tu
Feb 20
Lec 9
Themes
Th
Feb 22
Lec 10
Annotating charts
F
Feb 23
Work on project 1
6
Tu
Feb 27
No class (February Break)
Th
Feb 29
Lec 11
Presentation-ready plots
F
Mar 1
Present project 1
7
Tu
Mar 5
Lec 12
Accessible visualization
Th
Mar 7
Class cancelled (sickness)
F
Mar 8
hw-04
8
Tu
Mar 12
Lec 13
Visualizing spatial data (I)
Th
Mar 14
Lec 14
Visualizing spatial data (II)
F
Mar 15
Develop project 2 proposals
9
Tu
Mar 19
Lec 15
Time series data
Th
Mar 21
Lec 16
Animated visualizations
F
Mar 22
hw-05
10
Tu
Mar 26
Lec 17
Extending Quarto
Th
Mar 28
Lec 18
Interactivity: basic charts + dashboards
F
Mar 29
Peer review project 2 proposals
11
Tu
Apr 2
No class (Spring Break)
Th
Apr 4
No class (Spring Break)
F
Apr 5
No class (Spring Break)
12
Tu
Apr 9
Lec 19
Interactive reporting with Shiny (I)
Th
Apr 11
Lec 20
Interactive reporting with Shiny (II)
F
Apr 12
13
Tu
Apr 16
Lec 21
Interactive reporting with Shiny (III)
hw-06
Th
Apr 18
Lec 22
ML interpretation + regression models
F
Apr 19
14
Tu
Apr 23
Lec 23
Interpreting machine learning models
Th
Apr 25
Lec 24
Explaining predictions from machine learning models