Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data.
Exploratory Data Analysis with Seaborn
Taught in English
Instructor: Snehan Kekre
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Recommended experience
(434 reviews)
What you'll learn
Identify and interpret inherent quantitative relationships in datasets
Produce and customize various chart types with Seaborn in Python
Apply graphical techniques in exploratory data analysis (EDA)
Skills you'll practice
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Guided Project
Recommended experience
(434 reviews)
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Importing Data
Separate Target from Features
Diagnosis Distribution Visualization
Visualizing Standardized Data with Seaborn
Violin Plots and Box Plots
Use Joint Plots for Feature Comparison
Observing Distributions and their Variance with Swarm Plots
Obtaining all Pairwise Correlations
Recommended experience
Some experience in programming in Python.
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Practice new skills by completing job-related tasks.
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Follow along with pre-recorded videos from experts using a unique side-by-side interface.
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Available only on desktop
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