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Do you want to make beautiful, informative visualizations with ease? If so, then you must learn seaborn! Seaborn is a visualization library that is an essential part of the python data science toolkit. In this course, you will learn how to use seaborn's sophisticated visualization tools to analyze multiple real world datasets including the American Housing Survey, college tuition data, and guests from the popular television series, The Daily Show. Following this course, you will be able to use seaborn functions to visualize your data in several different formats and customize seaborn plots for your unique needs.
Introduction to the Seaborn library and where it fits in the Python visualization landscape.Introduction to Seaborn50 xpSeaborn foundation50 xpReading a csv file100 xpComparing a histogram and distplot100 xpUsing the distribution plot50 xpPlot a histogram100 xpRug plot and kde shading100 xpInterpreting the results50 xpRegression Plots in Seaborn50 xpCreate a regression plot100 xpPlotting multiple variables100 xpFacetting multiple regressions100 xp
Customizing Seaborn Plots
Overview of functions for customizing the display of Seaborn plots.Using Seaborn Styles50 xpSetting the default style100 xpComparing styles100 xpRemoving spines100 xpColors in Seaborn50 xpMatplotlib color codes100 xpUsing default palettes100 xpColor Palettes50 xpCreating Custom Palettes100 xpCustomizing with matplotlib50 xpUsing matplotlib axes100 xpAdditional plot customizations100 xpAdding annotations100 xpMultiple plots100 xp
Additional Plot Types
Overview of more complex plot types included in Seaborn.Categorical Plot Types50 xpstripplot() and swarmplot()100 xpboxplots, violinplots and lvplots100 xpbarplots, pointplots and countplots100 xpRegression Plots50 xpRegression and residual plots100 xpRegression plot parameters100 xpBinning data100 xpMatrix plots50 xpCreating heatmaps100 xpCustomizing heatmaps100 xp
Creating Plots on Data Aware Grids
Using Seaborn to draw multiple plots in a single figure.Using FacetGrid, factorplot and lmplot50 xpBuilding a FacetGrid100 xpUsing a factorplot100 xpUsing a lmplot100 xpUsing PairGrid and pairplot50 xpBuilding a PairGrid100 xpUsing a pairplot100 xpAdditional pairplots100 xpUsing JointGrid and jointplot50 xpBuilding a JointGrid and jointplot100 xpJointplots and regression100 xpComplex jointplots100 xpSelecting Seaborn Plots50 xp
In the following tracksData Scientist
DatasetsUS Housing and Urban Development FY 2018 Fair Market RentWashington DC Bike Share2018 College Scorecard TuitionDaily Show GuestsAutomobile Insurance Premiums2010 US School Improvement Grants
PrerequisitesData Manipulation with pandas
Creator of Practical Business Python
Chris is an active python user with over ten years of experience using python for everything from web development to system administration and most recently data science. He is the author of the popular blog Practical Business Python where he describes how to use python's data science tools to solve common business problems. He has degrees in Electrical Engineering and Computer Science from Vanderbilt University and an MBA from the University of Minnesota.
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