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:

• SAS Data Viz Guide
• Professor Shafer's Guide to Viz Attributes
• Tableau's Guide to Data Viz
• Documentation for Matplotlib, Seaborn, and Pandas Plotting