Data Visualization with Python: Crash Course

Learn to build interactive charts with Plotly and Python asap
Rating: 4.0 out of 5 (4 ratings)
1,500 students
English [Auto]

Learn to create interactive charts with Plotly
Learn to build dummy datasets like Fake Stock market price simulator
Learn to create vertical and horizontal bar charts
Learn to create vertical and horizontal grouped and stacked bar charts
Learn to create scatter charts
Learn to create line charts
Learn to create time series charts
Learn to create pie, donut and sunburst charts
Learn to create financial charts like candlestick, OHLC and waterfall charts
Learn to create multiple line charts
Learn to create bubble and dot charts
Learn to use Plotly dash to integrate charts into web application
Learn to use chart studio


  • A desire to transform complicated data into beautiful charts
  • A little bit knowledge on python programming language
  • Must have Jupyter and other required packages and libraries


Data Visualization is one of the indispensable topic in Data Science and Machine learning. It is process of creating interactive visuals to understand trends, variations, and derive meaningful insights from the data. We all know that Data is the new oil. Likewise if oil is not processed it is of no use and to derive relevant insights from data for making critical business decisions, it should be in cleaned and processed.

Data visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more.

In this learning course, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.

  • Bar Charts (Horizontal and Vertical)

  • Line Charts

  • Pie Charts

  • Donut Charts

  • Scatter Charts

  • Grouped Bar Chart (Horizontal and Vertical)

  • Segmented Bar Chart (Horizontal and Vertical)

  • Time and series Chart

  • Sunburst Chart

  • Candlestick Chart

  • OHLC Charts

  • Bubble Charts

  • Dot Charts

  • Multiple Line Charts and so on.

Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model.

Who this course is for:

  • Entrepreneurs
  • Machine learning engineers
  • Big Data Analyst
  • Data Scientist
  • Everyone who loves to play with data just like me

Course content

5 sections12 lectures53m total length
  • Introduction
  • Setup Environment


Docker | Kubernetes | AWS | Azure | ML | Linux | Python
Pranjal Srivastava
  • 3.6 Instructor Rating
  • 2,348 Reviews
  • 82,382 Students
  • 66 Courses

I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.

I have authored 60+ courses with over 80,000+ students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.