Project: Exploratory Data Analysis with Seaborn

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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)

Clock1.5 hours
Comment Dots英语(English)

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. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


Data ScienceMachine LearningPython ProgrammingData AnalysisData Visualization (DataViz)



  1. Introduction and Importing Data

  2. Separate Target from Features

  3. Diagnosis Distribution Visualization

  4. Visualizing Standardized Data with Seaborn

  5. Violin Plots and Box Plots

  6. Use Joint Plots for Feature Comparison

  7. Observing Distributions and their Variance with Swarm Plots

  8. Obtaining all Pairwise Correlations


Your workspace is a cloud desktop right in your browser, no download required




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