Data visualization is becoming critical in today s world of Big Data. If you are a data analyst or a Big Data enthusiast and want to explore the various techniques of data visualization, then this Learning Path is for you! This Learning Path focus on building a variety of data visualizations using multiple tools and techniques!
Packt s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
Let s take a quick look at your learning journey. To start with, we will walk you through 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 will 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.
Then, we will focus on building a variety of data visualizations using multiple tools and techniques. This is where we will put the theory together with actual hands–on experience of creating effective visualizations. Our efforts will be spent on choosing the best display types for our dataset, and then applying best practice principles to our selected charts, maps, or network graphs. We will spend considerable time on some of the most useful chart types, followed by a section where we explore the multiple uses of maps as visualizations. Finally, we will focus on understanding network graphs, a powerful tool for displaying relationship data.
Instructor Details
Courses : 212
Specification: Learning Path: Big Data Visualization
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2 reviews for Learning Path: Big Data Visualization
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Price | $12.99 |
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Provider | |
Duration | 5 hours |
Year | 2017 |
Level | Intermediate |
Language | English |
Certificate | Yes |
Quizzes | Yes |
$94.99 $12.99
Anne Myrcel Reyes Martinez –
good points were discussed, i would have preferred it was structured to start with the topics of understanding or knowing the data first before we push with the different kind of charts to use depending on what the analyst or what you learned from the data.
Mark Jordan Sabino –
more or simpler examples could have been much helpful in understanding the topics