Data Visualization Projects in Python with Plotly and Seaborn

This is a curated collection of Guided Projects for aspiring data scientists, data analysts, and anyone who is interested in both data visualization and dashboarding. This collection will help you get familiar with exploratory data analysis and visualization of datasets like Box Office, using Python libraries like Plotly and Seaborn.

You’ll start by analyzing Box Office data using Plotly and Seaborn, and then you’ll explore the data visualization capabilities of Plotly Express. You’ll see how to use these 2 libraries for exploratory data analysis (EDA), feature engineering, as well as statistical data visualization.

This collection is suitable even if you have never used Plotly and Seaborn before. Through step-by-step guidance from a subject matter expert, you will become comfortable using these libraries to generate interactive, publication-quality graphs and data analysis. Prior experience in Python programming is highly recommended.

Data Visualization Projects in Python with Plotly and Seaborn

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Analyze Box Office Data with Seaborn and Python

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指导项目

评分为 4.5（满分 5 星）。181 条评论

Intermediate Level

Data Visualization with Plotly Express

Coursera Project Network

指导项目

评分为 4.7（满分 5 星）。269 条评论

Intermediate Level

Why is data visualization important?

Producing visualizations is an important first step in exploring and analyzing real-world data sets. Data visualization is a key component of any data scientist's toolbox. It is also a powerful way to identify problems in analyses and illustrate results.

Are Plotly and Seaborn the right tools for me?

Many tools are needed to accomplish the variety of visualizations you can create using Python. Seaborn and Plotly focus on data exploration through rapid iteration. They support a wide range of visualizations including financial, statistical, geographic use-cases and even advanced three-dimensional use-cases. Both Seaborn and Plotly integrate with Jupyter widgets, making it possible to display plots in Jupyter notebooks and update them in-place after they are created. They have really rich documentation to integrate with Jupyter Notebooks and JupyterLab -- one of the most widely used tools by the data science and machine learning community.

What is a Guided Project?

A Guided Project helps you learn a job-relevant skill in under 2 hours through an interactive experience with step-by-step instructions from a subject matter expert. Everything you need to complete a Guided Project is available right in your browser. No software or prior experience is required to get started.

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