Learning Path: Big Data Visualization
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Learning Path: Big Data Visualization

Gain hands-on experience in creating effective visualizations of your data
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
0 students enrolled
Created by Packt Publishing
Last updated 8/2017
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
  • 5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
  • Understand, validate, and optimize your data for effective visualizations
  • Find out the best ways to maximize the impact of basic chart types
  • Get to know to optimize map displays
  • Create, build, and optimize network graphs using connected data
View Curriculum
  • Basic knowledge of HTML, CSS, and JavaScript would be helpful, but is not manadatory
  • Some experience with database, spreadsheet, and presentation software will be beneficial

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:

  • 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

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.

Who is the target audience?
  • This Learning path is for data analysts and data enthusiasts who are looking to learn what is data visualization and its different techniques.
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Curriculum For This Course
45 Lectures
Learning Data Visualization
21 Lectures 02:25:46

This video gives an overview of the entire course.

Preview 03:47

In this video, we will demonstrate why data visualization is essential to discovering and understanding patterns in datasets. We will walk through a process where raw data is transformed into an easily understood chart.
Visualizing Is Critical to Understanding

In this video, we will examine how we can take a very large dataset and employ several data visualization approaches to add context and meaning to the data.
Taming Big Data through Visualization

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.

Utilizing Visualization Tools

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.

Preview 05:05

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. 

Designing for Visual Clarity

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.

Driving User Focus

The effectiveness of a data visualization can often be enhanced through the effective and accurate use of element sizing.
Working with Element Sizing

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.

Employing Color Effectively

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.

Preview 07:39

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.

Categorical Data

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.

Time Series Data

In this video we will look at point data and learn how to recognize, understand, and ultimately display it effectively.

Point (X-Y) Data

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.

Geospatial Data

We'll cover the background, structure and use of network data in this video. Once the basics of data structure have been covered, we'll learn how to create compelling visualizations.
Network Data

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.

Unstructured Data

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.

Preview 09:05

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.

Bar Charts

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.

Scatter Plots

A distribution plot can be defined as any sort of chart that maps data points along a value axis. In many cases, this will be a single variable with many values accrued over time, plotted to understand the overall pattern.
Distribution Plots

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.

Dot Plots

Test Your Knowledge
5 questions
Data Visualization Techniques
24 Lectures 02:35:45
This video provides an overview of the entire course.
Preview 03:25

In this video, we emphasize the importance of understanding your source data, including its origin, layout, and field structure.
Understanding the Data

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.

Preparing the Data

In this video, our focus is on data validation, an important step in the pre-visualization process. We will discuss several data validation approaches to ensure clean data for our visualizations.
Validating the Data

Once our data prep and validation is complete, we should focus on selecting the best possible display type to represent the existing dataset.
Selecting the Best Display Option

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.

Creating Effective Line Charts

Bar charts can be used in many situations to compare categorical data in a highly effective, easy to understand manner. In this video, we’ll look at simple bar charts, as well as stacked charts, sparkbars, and small multiple bar charts.
Building Powerful Bar 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.

Designing Effective Dot Plots

Distribution plots display large numbers of data point observations, making them very useful for spotting anomalies in a series. In this video, we’ll discuss how and when to use these plots in our data visualization efforts.
Building Distribution Plots

Scatterplots are an essential component of the data visualization toolkit, providing the best approach to visualizing data points along the x- and y-axes. In this video, we’ll examine their use, as well as the use of bubble plots, a variation on the scatterplot approach.
Creating Effective Scatterplots

Box plots facilitate the summarization of the same sort of data we will often display using distribution plots. In this video, we’ll discuss and examine how to use box plots for showing aggregate patterns and key statistical distribution metrics.
Working with Box Plots

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.

Mastering Bullet Graphs

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.

Understanding Your Map Data

Dot density maps are one of the most important display types for geospatial data. These maps are based on lat/lon data, or sometimes centroid data, and are highly useful for displaying frequency data at discrete geographic levels.
Building Dot Density Maps

Categorical maps are a specialized type of Choropleth map, where map values are highlighted based on the specific categorical data in a dataset. This might be based on political preferences, top car brands, or any dataset where we can display a single ranked value.
Creating Categorical Maps

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.

Designing Choropleth Maps

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.

Enhancing Your Map

This video discusses well-designed maps that should be shared with a larger community of viewers in order to communicate important stories as well as to solicit critical feedback and dialogue.
Sharing a 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.

Creating and Procuring Network Data

This video will focus on the building of a network graph, using the type of dataset created in the previous video. We will have a brief look at network structure, layout algorithms, and graph statistics.
Building a Network Graph

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.

Understanding Graph Metrics

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.

Styling the Graph by 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.

Styling the Graph Using Color

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.

Sharing the Graph


Test Your Knowledge
5 questions
About the Instructor
Packt Publishing
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Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

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