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.
By the end of this Learning Path, you will have a strong understanding of how to effectively visualize your data.
About the Author
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 has built many visualizations for his personal websites, especially utilizing Gephi and Sigma.js to explore and visualize network data. 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. 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.
There are many online sources where users can find data and tools for developing data visualization skills. In this video, we will highlight some of the best available options.
In this video, we will introduce some visualization best practices that will start you on the path to creating effective visualizations. Our focus will be on understanding how to use spacing, focus, size, and color effectively.
Data visualizations should be designed so that users can easily interpret the message. The first step in this process is making sure that unnecessary visual distractions are minimized or eliminated.
End users should be able to easily understand the key messages within a data visualization. We can aid users in this pursuit by offering visual cues that help them to focus on important elements in the visualization.
The intelligent use of color can enhance most data visualization attempts. Used in conjunction with the concepts of clarity, focus, and sizing, color can help complete an effective data visualization.
Before we can create great data visualizations, it is imperative to be familiar with the underlying data. Knowing the data type will allow us to choose the best way to display the data.
We will show some examples of categorical data, so that we can begin to understand how it is used and where it is likely to be found. After gaining an understanding of these examples, we can then begin applying our knowledge.
To visualize time series data, we need to first be able to identify it within a dataset. From there, it is important to know what level of granularity is required by the end user, so that we can create the appropriate visualization.
In this video we will look at point data and learn how to recognize, understand, and ultimately display it effectively.
In this video we will learn to identify geospatial data and the elements that characterize it. We will look at the multiple types of geo data and gain an understanding of how and when to use it to our best advantage.
This video will focus on the rise of unstructured data. We'll learn many types and sources of this type of data, and then explore how it can be effectively visualized.
Line charts are one of the most commonly used chart types, and are especially useful for display of time series data. Yet there are many opportunities to turn a basic line chart into a more effective visualization.
Bar charts are perhaps the most frequently encountered chart type, but are often ineffective at telling a story. We will learn some simple steps to make bar charts both more effective and visually attractive.
Scatterplots are most commonly used to show relationships between data pairs using an x-y (and sometimes z) set of axes. Using this approach, we can compare numeric data tied to categorical variables such as stores, departments, countries, and so on, and see the relationship between them.
A dot plot chart should not be confused with statistical plots that have often used the same name. Data visualization dot plots are charts that can often replace bar charts in showing multiple comparative data points on a single axis.
In this video, we focus on the steps involved in preparing data for a visualization, including modifying field types, validating data values, and creating derived values to enhance our data visualization opportunities.
In this video, the focus is on optimizing line charts of various forms for maximum visual impact. We’ll look at simple, multiple, sparklines, and small multiple versions of line charts.
Dot plots can be employed in specific situations to display data that might otherwise be shared using bar charts. We’ll discuss when and how to create these useful displays.
Bullet graphs represent a recent effort to elegantly display data values relative to target or other threshold values. In this video, we’ll examine their use and show relevant examples.
In this video, we emphasize the importance of understanding your map data. We will examine some map datasets of both point and polygon varieties and what their structure looks like.
Choropleth maps are most frequently used to display numeric data using polygon-based geographic boundaries. We will look at use cases and examples of Choropleth maps in this video.
Maps require some enhancements in order to become fully useful data visualizations. These enhancements are often in the form of titles, legends, annotations, or other embellishments that support the meaning in the map.
In this video, we discuss the unique structure of network data and how to create network datasets for visualization. Locating and downloading existing network data files is also covered.
Graph statistics can be used primarily to understand the structure of the graph and its members. These same statistics can also be used to help make the graph more visually compelling and able to communicate stories.
As with conventional charts, size can be used to communicate powerful visual stories. In this video, we’ll take a look at how size can be used in network graphs, and discuss the use of graph statistics in sizing elements.
As with conventional charts, color can be a powerful tool in communicating visual stories. In this video, we’ll look at the use of color in network graphs, and discuss the use of graph statistics in coloring elements.
Network graphs, similar to other visualizations, gain power through being shared with a wider audience. This video will show a number of options for sharing completed graphs and their data structures.
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