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Data Visualizations with Python

Unit 2: Required


Materials We Provide

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

Learning Objectives

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

Student Requirements

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

Lesson Outline

Outline: Part 1 (Data Visualization Overview)

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

Outline: Part 2 (Guided Practice Lab)

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

Additional Resources

For more information on this topic, check out the following resources: