MISSION 286

Visualizing Frequency Distributions

In the Frequency Distributions Mission, we learned that while frequency tables can be useful, we have to look up the frequency of each unique value or class interval and at the same time compare the frequencies. This process can become time-consuming for tables with many unique values or class intervals, or when the frequency values are large and hard to compare against each other.

We can solve this problem by visualizing the data in the tables with the help of graphs. Graphs make it much easier to scan and compare frequencies, providing us with a single picture of the entire distribution of a variable.

Because they are easy to grasp and also eye-catching, graphs are a better choice over frequency tables if we need to present our findings to a non-technical audience.

In this mission, we'll learn about three kinds of graphs:

  • Bar plots
  • Pie charts
  • Histograms

By the end of the mission, we'll know how to generate the graphs ourselves, and we'll know when it makes sense to use each one. 

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 why visualizing distributions is important.
  • Learn how to generate bar plots, pie charts, and histograms.
  • Learn when to use bar plots, pie charts, and histograms.

Mission Outline

1. Visualizing Distributions
2. Bar Plots
3. Horizontal Bar Plots
4. Pie Charts
5. Customizing a Pie Chart
6. Histograms
7. The Statistics Behind Histograms
8. Histograms as Modified Bar Plots
9. Binning for Histograms
10. Skewed Distributions
11. Symmetrical Distributions
12. Next Steps
13. 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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