1. Wrap-Up
In this course, you've learned how to improve your Python visualizations by making them easier to read and more impactful.
2. Highlighting data
In Chapter 1, we went over when and how to highlight your data using both color and text.
3. Using color responsibly
In the second chapter, we talked all about color. How you should use different palette types for different data types and how you need to be mindful of color blindness.
4. Showing uncertainty
In the third chapter, we dove into the complexities of displaying uncertainty in your visualizations. Using bars and bands to represent confidence intervals and using multiple points and lines to show resampling.
5. The visualization process
Finally, we covered the process of visualization in the data science workflow, from messy exploratory visualizations to exploring potential trends and finally polishing your plot for presentation.
6. Going further
If you want to keep learning more about visualization, various blogs such as flowing data and data wrapper provide fantastic tutorials and highlight great examples of visualization.
Another resource is the Twitter hashtag dataviz. There is an active community constantly posting and commenting on cool and inspiring projects.
7. Thank you!
With the combination of Pandas, matplotlib, and Seaborn there is very little you can't do with visualization in Python, and now you know how to make those visualizations the star of your data science projects.
Thank you for taking this course, and I can't wait to see what you produce.