Basics of Plotly for Data Analysis & Data Science

Learn basics of Plotly , frequent used Plotly plots , styling in plotly with the help of real-world use-cases


What You Will Learn

Learn about Essentials of Plotly to create plots like Bar Charts, Line Charts, Scatter Plots, Histogram ,distribution plots , and more!

How to Create Layouts with Plotly’s Dash library.

How to Deploy your interactive dashboards

Learn styling in Plotly


  • Have a Keen Desire to learn !


In this course, you will learn how to create interactive Visuals in python using the plotly data visualizations library and dash library.

This course will teach your everything you need to know to use Python to create interactive dashboard’s with Plotly’s new Dash library! Have you ever wanted to take your Python skills to the next level in data visualization? With this course you will be able to create fully customization plots , interactive dashboards with the open source libraries like Plotly ..

Data visualisation is very critical for generating and communicating easy to understand finding and insights. Either you are a Data Analyst who wants to create a dashboard/present your analysis or you are a Data Scientist who wants to create a UI for your machine learning models, plotly dash can be a boon for both.

You will learn in this course many chart types..

  • Bar chart
  • Line cart
  • Pie chart
  • Scatter plot
  • Histogram
  • Box plot
  • Violin plot
  • Distribution (KDE) Plot

We’ll start off by teaching you enough Python and Pandas that you feel comfortable working and generating data  Then we’ll continue by teaching you about basic data visualization with Plotly, including scatter plots, line charts, bar charts , box plots, histograms, distribution plots and more! We’ll also give you an intuition of when to use each plot type.

Using the plotly dash, you can create interactive dashboards using python without knowing HTML, CSS and Javascript. Creating plotly dash dashboards is so simple and easy that you can create your dashboards within hours..

By taking this course you will be learning the bleeding edge of data visualization technology with Python and gain a valuable new skill to show your colleagues or potential employers.

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Who this course is for:

  • Any Python programmers who want to present their analyses in interactive web-based dashboards

Course content

7 sections • 18 lectures • 1h 53m total length

  • Introduction & Course benefits !


  • Quick Summary of Jupyter Notebook


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