Introduction to Data Visualization with Plotly in Python

Create interactive data visualizations in Python using Plotly.

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4 Hours14 Videos45 Exercises4,405 Learners
3550 XP

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Course Description

Producing high-quality, interactive visualizations historically required complex code, extensive time, and effort. Not anymore. In this course, you’ll learn how to create publication-quality graphs harnessing the power of JavaScript, without leaving the comfort of the Python programming language we all love. You’ll create, style, and customize a variety of stunning, interactive graphs—using datasets ranging from stock prices to basketball team stats, and even penguin beak sizes! Using the Plotly charting library, you’ll also learn to customize interactivity such as hover information, range sliders, custom buttons, and even drop-downs that reactively change the visualization. Are you ready to level-up your data visualization skills?

  1. 1

    Introduction to Plotly

    Free

    Enter the world of Plotly! In this first chapter, you’ll learn different ways to create plots and receive an introduction to univariate plots. You’ll then build several popular plot types, including box plots and histograms, and discover how to style them using the Plotly color options.

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    Plotly and the Plotly Figure
    50 xp
    Playing with a Plotly Figure
    50 xp
    Fixing a Plotly figure
    100 xp
    Univariate visualizations
    50 xp
    Student scores bar graph
    100 xp
    Box plot of company revenues
    100 xp
    Histogram of company revenues
    100 xp
    Customizing color
    50 xp
    What color did we use?
    50 xp
    Coloring student scores bar graph
    100 xp
    Side-by-side revenue box plots with color
    100 xp
    Revenue histogram with stacked bars
    100 xp
  2. 3

    Advanced Customization

    Take your Plotly graphs to the next level with more advanced customizations. Through hands-on exercises, you’ll learn how to layer multiple interactive chart types in the same plot (such as a bar chart with line chart over the top). You’ll then create time-series selectors, such as year to date (YTD), to help you zoom in and out of your line charts.

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  3. 4

    Advanced Interactivity

    In this final chapter, you’ll harness Plotly’s advanced user interactivity as you learn how to create buttons, dropdowns, and sliders that change everything from graph types to annotations and much more. Take your Plotly skills to the next level and build truly interactive user experiences.

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Datasets

Penguins dataRevenue dataAAPL dataWorld Bank population dataSydney temperature dataRainfall dataMonthly sales

Collaborators

Amy Peterson

Prerequisites

Intermediate Python
Alex Scriven Headshot

Alex Scriven

Data Scientist @ New South Wales Government

Alex is a Data Scientist working for the New South Wales Government in Sydney, Australia. He is also a Lecturer at the University of Technology Sydney where he teaches into several courses in their Master of Data Science & Innovation program in machine learning & deep learning. He is also a partner of a boutique data analytics firm, Madlytics and is passionate about building communities around technology & entrepreneurship.
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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.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA