Data Visualization, Part 1 by
Do you communicate data and information to stakeholders? This issue is Part 1 of a two-part series on data visualization and evaluation. In Part 1, we introduce recent developments in the quantitative and qualitative data visualization field and provide a historical perspective on data visualization, its potential role in evaluation practice, and future directions. It discusses: Quantitative visualization methods such as tree maps Sparklines Web-based interactive visualization Different types of qualitative data visualizations, along with examples in various evaluation contexts A toolography describing additional data visualization tools and software, along with their major strengths and limitations. Intended as a guidance for understanding and designing data visualizations, this issue introduces fundamental concepts and links them to daily practice. This is the 139th volume of the Jossey-Bass quarterly report series New Directions for Evaluation, an official publication of the American Evaluation Association.
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Data Visualization by
Data Visualization: A Guide to Visual Storytelling for Libraries is a practical guide to the skills and tools needed to create beautiful and meaningful visual stories through data visualization. Learn how to sift through complex datasets to better understand a variety of metrics, such as trends in user behavior and electronic resource usage, return on investment (ROI) and impact metrics, and data about library collections and repositories. Sections include: - Identifying and interpreting datasets for visualization - Tools and technologies for creating meaningful visualizations - Case studies in data visualization and dashboards Data Visualization also features a 20-page color insert showcasing a wide variety of visualizations generated using an array of data visualization technologies and programming languages that can serve as inspiration for creating your own visualizations. Understanding and communicating trends from your organization's data is essential. Whether you are looking to make more informed decisions by visualizing organizational data, or to tell the story of your library's impact on your community, this book will give you the tools to make it happen.
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Data Visualization: a Successful Design Process by This link opens in a new window This link opens in a new window
Persuading with Data by
An integrated introduction to data visualization, strategic communication, and delivery best practices. Persuading with Data provides an integrated instructional guide to data visualization, strategic communication, and delivery best practices. Most books on data visualization focus on creating good graphs. This is the first book that combines both explanatory visualization and communication strategy, showing how to use visuals to create effective communications that convince an audience to accept and act on the data. In four parts that proceed from micro to macro, the book explains how our brains make sense of graphs; how to design effective graphs and slides that support your ideas; how to organize those ideas into a compelling presentation; and how to deliver and defend data to an audience. Persuading with Data is for anyone who has to explain analytical results to others. It synthesizes a wide range of skills needed by modern data professionals, providing a complete toolkit for creating effective business communications. Readers will learn how to simplify in order to amplify, how to communicate data analysis, how to prepare for audience resistance, and much more. The book integrates practitioner and academic perspectives with real-world examples from a variety of industries, organizations, and disciplines. It is accessible to a wide range of readers--from undergraduates to mid-career and executive-level professionals--and has been tested in settings that include academic classes and workplace training sessions.
This link opens in a new window This link opens in a new windowData visualization uses visual tools like charts, graphs, and maps to help users quickly interpret sets of data. Encoding aspects of the data in visual elements like position, size, shape, and color data visualization can help a reader grasp a trend or correlation intuitively and quickly, even if the original data are large and complex.
The right tool for your project depends on the question you want to ask, type of data you have to work with, and your comfort level with coding. Below you'll find some tools you can use to get started, but this is in no way an exhaustive list.
If your data is mostly or totally numeric and you want to use a point-and-click tool instead of coding, try these:
Excel is a classic tool for visualizing data, and it continues to be a popular option for researchers, particularly those in business fields. This libguide from Duke University Libraries introduces the visualization capabilities of Excel well.
Tableau handles the following data types:
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Tableau uses powerful analytics to generate graphics, and it's great for building data dashboards that can be embedded in other digital projects. Be sure to check out our LibGuide for more information on getting and using Tableau.
R is a statistical computing and analytics environment that can be used to both analyze your data and produce graphics. The ggplot2 package is a great option for producing graphics, and you can enhance its capabilities by adding these extension packages.
Python can also be used to analyze data and produce visualizations. Its most well-known viz library is matplotlib, but there are numerous other visualization libraries you may want to investigate. If you're working with text data, the nltk and TextBlob libraries are useful for both analysis and visualization. Check out our Python LIbGuide for more information.
