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Learning Data Visualization [Video]

More Information
Learn
  • Understand why data visualization is important
  • Know how to use data visualization with big data
  • Find out how to utilize visualization best practices
  • Discover how to identify and understand your source data
  • Get to know how to match your dataset to the appropriate visualization type
  • See how to optimize basic chart types for maximum impact
About

In this course, we’ll walk through why data visualization is so critical in today’s world of big Data. This is followed by an overview of the basic principles of data visualization, why they are important, and how they can be used to make visualizations highly effective. We’ll then walk you through some of the basics –such as how to build visualizations using best practices . You'll also learn how to identify data types and match them with the appropriate display formats. By the end of this course, you will have a strong understanding of how to begin building effective data visualizations, based on a solid understanding of successful design principles.

Style and Approach

We will walk through an overview on why data visualization is critical to understanding, followed by a primer on best practices. This will be followed by sections where we learn how to better understand our source data, which we can then match to the appropriate visual formats. We’ll conclude by learning how we can get the most out of basic visualizations by optimizing elements such as size, color, and spacing.

Features
  • Learn why data visualization is important, and how it can be used to manage big data
  • Learn best practices in data visualization, and apply them to your own displays
  • Learn how to assess data sources, and match them to the best display types
  • Improve your ability to design and perfect basic chart types1
Course Length 2 hours 25 minutes
ISBN 9781787280960
Date Of Publication 24 Feb 2017

Authors

Ken Cherven

Ken Cherven has been creating data visualizations for more than 10 years using a variety of tools, including Excel, Tableau, Cognos, D3, Gephi, Sigma.js, and Exhibit, along with geospatial tools such as Mapbox, Carto, and QGIS.

He uses Tableau on a daily basis in his current position, where he has built dozens of performance dashboards to track both marketing and operational metrics. He has also built many visualizations for his personal websites, especially utilizing Gephi and Sigma.js to explore and visualize network data.

He is very interested in tools related to the exploration of network data, typically using Gephi for most of his current output. He’s also interested in text analysis, where he’s used tools such as Aylien, RapidMiner, R, and Exploratory to begin understanding and visualizing underlying patterns in political speeches, email transmissions, and book content.

His experience in building data visualizations has intersected with many technologies, including a variety of SQL-based tools and languages including Oracle, MySQL, and SQLServer. He frequently edits and styles network information using HTML and CSS, along with a bit of JavaScript.

He is also highly engaged in the world of data visualization, including but not limited to his daily work experience. His work is based on a thorough understanding of visualization principles learned through extensive reading and practice. He also uses his websites to display and promote visualizations, which he shares with a wider audience. He has previously authored two books on Gephi for Packt, and has also presented at multiple data visualization conferences