Exploratory Data Analysis with Seaborn
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)
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.
Les compétences que vous développerez
- Data Science
- Machine Learning
- Python Programming
- Data Analysis
- Data Visualization (DataViz)
Apprendrez étape par étape
Votre enseignant(e) vous guidera étape par étape, grâce à une vidéo en écran partagé sur votre espace de travail :
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
Comment fonctionnent les Projets Guidés
Votre espace de travail est un bureau cloud situé dans votre navigateur, aucun téléchargement n'est requis.
Votre enseignant(e) vous guide étape par étape dans une vidéo en écran partagé
Avis
Meilleurs avis pour EXPLORATORY DATA ANALYSIS WITH SEABORN
This project is great for people go want to advances her career exploring new viz techniques. The instructor is great, clear and easy to follow. I will definitely recommend to take this project.
As a beginner, this was a very good insight into EDA for me. You will however, have to read the documentation and more articles to go in-depth. However, this is a very good introductory course.
I think it would be nice if I could play the video while using jupyter on my computer, it was a bit annoying to use the virtual machine, since if it was not on the page the video stopped
This was my first guided project . It was a nice experience and the course material was truly helpful for me. The instructor's pace of teaching was absolutely stunning.
Foire Aux Questions
Foire Aux Questions
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