Unit 2: Required
Materials We Provide
|Lesson||High-level overview (ipynb slides)||Here|
|Practice Activity||Instructor-run lab with exercises||Here|
|Solution||Solutions to the lab exercises.||Here|
|Datasets||Standard UCI Boston housing||Here|
|Generic quarterly sales data||Here|
|UFO sighting records||Here|
|Drinks consumed per capita in various countries||Here|
This lesson purposefully uses a large number of datasets. This allows students to practice opening different types of data files. Having many datasets available allows us to explore a variety of visualizations throughout the lesson that might not be present in one dataset alone.
Part 1: Data Visualization Overview
- Describe why data visualization is important.
- Identify the characteristics of a great data visualization.
- Describe when you would use a bar chart, pie chart, scatter plot, and histogram.
Part 2: Guided Practice (Lab)
- Practice using different types of plots.
- Use Pandas methods for plotting.
- Create line plots, bar plots, histograms, and box plots.
- Know when to use Seaborn or advanced Matplotlib
Before this lesson(s), students should already be able to:
- Recall and define the basic syntax for Pandas DataFrames and Series
- Demonstrate how to input sample python code in a jupyter notebook dataframe
Outline: Part 1 (Data Visualization Overview)
- Why Use Data Visualization? (5 mins)
- Anscombe's Quartet (10 mins)
- Attributes of Good Visualization (10 mins)
- Choosing the Right Chart (15 mins)
- Independent Research (see Part 2)
Outline: Part 2 (Guided Practice Lab)
- Line Plots (20 mins)
- Line Plots EXERCISE (10 mins)
- Bar Plots (15 mins)
- Bar Plots EXERCISE (10 mins)
- Histograms (15 mins)
- Histograms EXERCISE (10 mins)
- Grouped Histograms (10 mins)
- Box Plots (20 mins)
- Grouped Box Plots (5 mins)
- Using Seaborn (5 mins)
- Scatter Plot EXERCISE (10 mins)
- OPTIONAL: Understanding Matplotlib (if extra time)
- OPTIONAL: Additional Topics (if extra time)
For more information on this topic, check out the following resources: