Course Schedule#

Class #

Date

Course Topics

Format

1

Monday January 10

Introduction to Data Visualization

Live Class (via Zoom)

2

Wednesday January 12

Importance of Data Visualizations

Live Class (via Zoom)

3

Monday January 17

Visual encodings and plot configuration

Live Class (via Zoom)

4

Wednesday January 19

Visualizing distributions fairly

Live Class (via Zoom)

5

Monday January 24

No Class

N/A

6

Wednesday January 26

No Class

N/A

5

Monday January 31

Principles of Effective Visualizations

Live Class (via Zoom)

6

Wednesday February 2

Trendlines, confidence intervals, and composite figures

Live Class (via Zoom)

7

Monday February 7

Seaborn Tutorial (Recorded Video)

N/A

8

Wednesday February 9

Q&A on Zoom

Live Q&A (via Zoom)

Learning Intentions#

Lecture 1 - Introduction to Data Visualization#

  1. Explain the importance of data visualizations.

  2. Describe the differences between Imperative and Declarative programming.

  3. Create basic visualizations in Altair.

Lecture 2 - Importance of data Visualizations#

Coming soon…

Lecture 3 - Visual encodings and plot configuration#

  1. Choose effective visual encodings.

  2. Visualize frequencies with bar plots.

  3. Facet to explore more variables simultaneously.

  4. Customize axes labels and scales.

  5. Introduced to Exploratory Data Analysis

Lecture 4 - Visualizing distributions fairly#

  1. Visualize distributions.

  2. Understand how different distribution plots are made and their pros and cons.

  3. Select an appropriate distribution plot for the situation.

  4. Grasp EDA on a conceptual levels both for numerical and categorical variables.

  5. Create density plots to compare a few distributions.

  6. Create boxplots and violin plot to compare many distributions.

  7. Use repeated plot grids to investigate multiple data frame columns in the same plot.

  8. Visualize correlations and counts of categorical variables.

Lecture 5 - Principles of Effective Visualizations#

  1. Follow guidelines for best practices in visualization design.

  2. Avoid over-plotting via 2D distribution plots.

  3. Adjust axes extents and formatting.

  4. Modify titles of several figure elements.

Lecture 6 - Colours#

  1. Choose appropriate color schemes for your data.

  2. Use pre-made and custom color schemes.

  3. Selectively highlight and annotate data with color and text.

  4. Directly label data instead of using legends.

Lecture 7 - Trendlines, confidence intervals, and composite figures#

  1. Visualize pair-wise differences using a slope plot.

  2. Visualize trends using regression and loess lines.

  3. Create and understand how to interpret confidence intervals and confidence bands.

  4. Telling a story with data (reading only)

  5. Layout plots in panels of a figure grid.

  6. Save figures outside the notebook.

  7. Understand figure formats in the notebook.

  8. Retrieve info on further topics online.

Lecture 8 - Seaborn Tutorial#

  1. Practice using Seaborn to make data visualizations.