Topic | Description | Link |
---|---|---|
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.
- 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.
- 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
40 mins
- 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)
- Conclusion
130 mins
- 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)
- Summary
For more information on this topic, check out the following resources: