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Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.
Introduction to SeabornFree
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.
Visualizing Two Quantitative Variables
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!Introduction to relational plots and subplots50 xpCreating subplots with col and row100 xpCreating two-factor subplots100 xpCustomizing scatter plots50 xpChanging the size of scatter plot points100 xpChanging the style of scatter plot points100 xpIntroduction to line plots50 xpInterpreting line plots100 xpVisualizing standard deviation with line plots100 xpPlotting subgroups in line plots100 xp
Visualizing a Categorical and a Quantitative Variable
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.
Customizing Seaborn Plots
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!Changing plot style and color50 xpChanging style and palette100 xpChanging the scale100 xpUsing a custom palette100 xpAdding titles and labels: Part 150 xpFacetGrids vs. AxesSubplots100 xpAdding a title to a FacetGrid object100 xpAdding titles and labels: Part 250 xpAdding a title and axis labels100 xpRotating x-tick labels100 xpPutting it all together50 xpBox plot with subgroups100 xpBar plot with subgroups and subplots100 xpWell done! What's next?50 xp
PrerequisitesIntroduction to Data Science in Python
Erin is a Data Scientist who is passionate about both statistics and education. She enjoys experimental design, communicating data analyses to a wide range of audiences, and developing user-facing data products for technology companies. Previously, she was a biostatistician for two epidemiological studies on cardiac arrest.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA