Introduction to Data Visualization with Seaborn
Learn how to create informative and attractive visualizations in Python using the Seaborn library.Start Course for Free
4 Hours14 Videos44 Exercises91,129 Learners3700 XPData Analyst with Python TrackData Scientist with Python TrackData Scientist Professional with Python TrackData Visualization with Python Track
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Create Your Own Seaborn PlotsSeaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations. This 4-hour course provides an introduction to how you can use Seaborn to create a variety of plots, including scatter plots, count plots, bar plots, and box plots, and how you can customize your visualizations.
Turn Real Datasets into Custom Seaborn VisualizationsYou’ll explore this library and create your Seaborn plots based on a variety of real-world data sets, including exploring how air pollution in a city changes through the day and looking at what young people like to do in their free time. This data will give you the opportunity to find out about Seaborn’s advantages first hand, including how you can easily create subplots in a single figure and how to automatically calculate confidence intervals.
Improve Your Data Communication SkillsBy the end of this course, you’ll be able to use Seaborn in various situations to explore your data and effectively communicate the results of your data analysis to others. These skills are highly sought-after for data analysts, data scientists, and any other job that may involve creating data visualizations. If you’d like to continue your learning, this course is part of several tracks, including the Data Visualization track, where you can add more libraries and techniques to your skillset.
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
In the following tracksData Analyst with PythonData Scientist with PythonData Scientist Professional with PythonData Visualization with Python
PrerequisitesIntroduction to Data Science in Python
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*4.7from 43 reviews
- Warit S.about 16 hours
Simple and easy to understand. The course is beginner friendly.
- Shreenidhi D.5 days
Very organised teaching
- Jose B.8 days
Was very usefull for my Data Analyst Career.
- Lu W.17 days
Great combination of lecture and practice! Very practical instructions
- Felipe G.28 days
All around I found this course very good. Videos, examples, exercises... all were in sync and I felt it was easy to learn. Also, the last video, were there are "next steps" and "related learning", was a great idea.
"Simple and easy to understand. The course is beginner friendly."
"Very organised teaching"
"Was very usefull for my Data Analyst Career."
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