MISSION 287

Comparing Frequency Distributions

In the Visualizing Frequency Distributions lesson, we learned what graphs we can use to visualize the frequency distribution of any kind of variable. In this mission, we'll learn how to compare frequency distributions with visualization. In addition, we will learn about the types of graphs we can use to compare multiple frequency distributions at once.

You will build on the concepts you learned in Visualizing Frequency Distributions, learning additional visualizations such as step-type histograms, kernel density plots, strip plots, and box plots. You will work with the seaborn visualization library, which is built on top of matplotlib. Seaborn has good support for more complex plots, attractive default styles, and integrates well with the pandas library.

In this lesson, we will continue to work with the WNBA dataset while learning how to compare frequency distributions to determine the most played positions on the court for rookies, as well as how rookies compare to veterans with respect to positions on the court.

As you work through each concept, you’ll apply what you’ve learned from within your browser; there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer-checking to ensure you've fully mastered each concept before moving on to the next.

Objectives

  • Learn to compare frequency distributions.
  • Learn about grouped bar plots.
  • Learn about step-type histograms.
  • Learn about kernel density estimate plots.
  • Learn about strip plots and box plots.

Mission Outline

1. Comparing Frequency Distributions
2. Grouped Bar Plots
3. Challenge: Do Older Players Play Less?
4. Comparing Histograms
5. Kernel Density Estimate Plots
6. Drawbacks of Kernel Density Plots
7. Strip Plots
8. Box plots
9. Outliers
10. Next steps
11. Takeaways

statistics-fundamentals

Course Info:

Beginner

The median completion time for this course is 6.75 hours. View Details

This course requires a basic subscription. This course includes five missions, and one guided project. It is the 15th course in the Data Analyst in Python and Data Scientist in Python path.

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