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Congratulations

1. Congratulations

Well done! You've seen a lot of data visualizations now.

2. Histograms: show a distribution

In Chapter 1 you learned that histograms are excellent for showing the distribution of a continuous variable, and

3. Box plots: show lots of distributions

that box plots can compactly show the distributions of lots of continuous variables.

4. Scatter plots: compare two numeric variables

In Chapter 2 you saw that scatter plots can show the relationship between two continuous variables, and

5. Line plots: show trends over time

that line plots are great for showing trends over time.

6. Bar plots: show counts by category

You also saw that bar plots show counts or proportions split by categories, and

7. Dot plots: show log scale metrics by category

that dot plots will do the same, but allow for logarithmic scales and showing multiple metrics at once.

8. Extra dimensions

In Chapter 3, you saw that using colors or multiple panels are often the best way to add a third dimension to your plot, since 3D plots are hard to interpret.

9. 3 types of color scale

You also learned that there are three types of color scale: qualitative, sequential, and diverging.

10. Pair plot: compare many variables

For cases where you need to analyze many variables at once, you saw three types of plots. Pair plots show relationships between each pair of variables,

11. Correlation heatmap: show related variables

correlation heatmaps show related variables, and

12. Parallel coordinates plot: find patterns across variables

parallel coordinates plots show patterns across many variables.

13. Rose plot: show a cyclical distribution

In Chapter 4, you learned that plots with polar coordinates are usually a bad idea, but they have niche uses when data is cyclical, like a time of day.

14. Dual axes are bad

You also learned that using dual axes is almost always misleading, and that

15. Eliminate chartjunk

minimalism is a good idea. You should eliminate anything from the plot that distracts from interpretations.

16. Next steps

If you are interested in learning more about data visualization, you can take courses on the R package ggplot2, which was used to create the plots in this course, or on business intelligence tools like Tableau, or Python packages like matplotlib and seaborn.

17. You made it!

Happy learning!