Well done! What's next?

1. Well done! What's next?

Congratulations on completing this introduction to Seaborn! Let's discuss the next steps you can take to build upon the skills that you've learned in this course.

2. Where does Seaborn fit in?

Seaborn is a powerful data visualization tool that allows you to create attractive and informative visualizations with just a few lines of code. Let's return to this diagram of the data analysis workflow to see where Seaborn fits in.

3. Where does Seaborn fit in?

As we've seen in our examples, Seaborn is great for both the initial exploration of your data and communicating the results at the end of your data analysis.

4. Next Steps: Explore and communicate results

In this course, we've covered the most common data visualizations used for data exploration. DataCamp has other visualization courses if you want to learn even more. For example, Seaborn also supports more advanced visualizations and analyses like linear regressions. We also learned that Seaborn was built on top of Matplotlib and practiced how to use some Matplotlib functions to customize Seaborn plots. Here, too, there are many more customizations that Matplotlib supports if you wish to learn more.

5. Next steps: Gather data

You can also learn more about the other steps of the data analysis workflow. If you wish to learn more about how to gather your data, explore courses on importing data in Python and SQL.

6. Next steps: Transform and clean

In this course, we learned that Seaborn works extremely well with tidy pandas DataFrames. There is more to learn here about how to get your data into pandas DataFrames, clean it, and transform it into a tidy format.

7. Next steps: Analyze and build models

Finally, I encourage you to learn more about statistical analysis. For example, for bar plots, Seaborn automatically calculates confidence intervals for each bar value. There is a lot to learn here about how these confidence intervals are calculated and how to interpret them.

8. Congratulations!

Though there is always more to learn, we've covered a great deal in this introduction to Seaborn. Congratulations on completing the course! I hope you enjoyed it and feel confident using Seaborn in the future for your data visualization needs.

This exercise is part of the course

Introduction to Data Visualization with Seaborn

BeginnerSkill Level

4.8+

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What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.

Exercise 1: Introduction to Seaborn Exercise 2: Making a scatter plot with lists Exercise 3: Making a count plot with a list Exercise 4: Using pandas with Seaborn Exercise 5: "Tidy" vs. "untidy" data Exercise 6: Making a count plot with a DataFrame Exercise 7: Adding a third variable with hue Exercise 8: Hue and scatter plots Exercise 9: Hue and count plots

In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!

Exercise 1: Introduction to relational plots and subplots Exercise 2: Creating subplots with col and row Exercise 3: Creating two-factor subplots Exercise 4: Customizing scatter plots Exercise 5: Changing the size of scatter plot points Exercise 6: Changing the style of scatter plot points Exercise 7: Introduction to line plots Exercise 8: Interpreting line plots Exercise 9: Visualizing standard deviation with line plots Exercise 10: Plotting subgroups in line plots

Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.

Exercise 1: Count plots and bar plots Exercise 2: Count plots Exercise 3: Bar plots with percentages Exercise 4: Customizing bar plots Exercise 5: Box plots Exercise 6: Create and interpret a box plot Exercise 7: Omitting outliers Exercise 8: Adjusting the whiskers Exercise 9: Point plots Exercise 10: Customizing point plots Exercise 11: Point plots with subgroups

In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!

Exercise 1: Changing plot style and color Exercise 2: Changing style and palette Exercise 3: Changing the scale Exercise 4: Using a custom palette Exercise 5: Adding titles and labels: Part 1 Exercise 6: FacetGrids vs. AxesSubplots Exercise 7: Adding a title to a FacetGrid object Exercise 8: Adding titles and labels: Part 2 Exercise 9: Adding a title and axis labels Exercise 10: Rotating x-tick labels Exercise 11: Putting it all together Exercise 12: Box plot with subgroups Exercise 13: Bar plot with subgroups and subplots Exercise 14: Well done! What's next?

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