Build Your First Data Visualization with Pygal 2
This quick course demonstrates just how easy it is to create a complex interactive data visualization for further analysis.
What you'll learn
Data Visualization is an amazing way to share your data insights and stories. In this course, Build Your First Data Visualization with Pygal, you will learn how to easily and quickly build an interactive data visualization with minimal code and effort. First, you will be installing Python via Anaconda, understanding dependencies, and how to make your data visualization interactive without leaving the browser. Next, you will be creating and styling your first chart with Pygal inside of Jupyter Notebooks. Finally, you will go over outputs so you can share your new data visualization with others. By the end of this course you will be able to quickly and easily build your own interactive data visualization.
Table of contents
Course FAQ
Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace.
Whether you’re new to building data visualization or looking to enhance your skills, this is the perfect place to get started.
The course will walk you through installing Python via Anaconda and then using Pygal inside of Jupyter Notebooks.
Interactive data visualization enables direct actions on a graphical plot to change elements and link between multiple plots.
Pygal is a Python module that creates SVG (Scalable Vector Graphics) graphs/charts in a variety of styles. Pygal is highly customize-able, yet also extremely simplistic, which is a very rare combination.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
About the author
Troy Kranendonk is a Curriculum Manager for Data Access and Analytics as well as an author with Pluralsight Troy studied Digital Media Education and loves to innovate and push the boundaries in with tech. Troy has taught motion graphics, film, graphic design, and 3D animation as well as Action Script, HTML, and CSS and currently teaching Data.