#datamustread: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics (2nd Edition) by Nathan Yau

A bookshelf neatly arranged with several books on data visualization and analytics: Displayed in the center is the 2nd edition of "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" by Nathan Yau. Surrounding this book are various other titles, including those by the Alexander Loth: "Decisively Digital", "Teach Yourself VISUALLY Power BI", "Visual Analytics with Tableau", "Datenvisualisierung mit Tableau", "Datenvisualisierung mit Power BI", and "KI für Content Creation." Other visible titles include "Rewired" and "Self-Service BI & Analytics." The arrangement highlights a strong focus on data visualization, analytics, and AI.
The 2nd edition of Visualize This by Nathan Yau, surrounded by several influential data and AI books, including my own works like Decisively Digital and Teach Yourself Visually Power BI.

While my latest book, KI für Content Creation, has just been reviewed by the renowned c’t magazine, I’m happy to continue reviewing books myself. Today, I’m reviewing the just-released second edition of a cornerstone of the data visualization community, Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau.

Visualize This: A Deep Dive into Data Visualization

Nathan Yau’s Visualize This has long been a staple for data enthusiasts, and the updated second edition brings fresh techniques, technologies, and examples that reflect the rapidly evolving landscape of data visualization.

Decisively Digital

Decisively Digital

Discover the power of artificial intelligence and digital transformation in the #1 best-selling business book Decisively Digital.

Buy on Amazon Learn More

Core Highlights of This Book

Data-First Approach: Yau emphasizes that effective visualizations start with a deep understanding of the data. This foundational principle ensures that the resulting graphics are not just visually appealing but also accurately convey the underlying information.

Diverse Toolkit: The book introduces a wide range of tools, including the latest R packages, Python libraries, JavaScript libraries, and illustration software. Yau’s pragmatic approach helps readers choose the right tool for the job without feeling overwhelmed by options.

Real-World Applications: With practical, hands-on examples using real-world datasets, readers learn to create meaningful visualizations. This experiential learning approach is particularly valuable for grasping the subtleties of data representation.

Comprehensive Tutorials: The step-by-step guides are a standout feature, covering statistical graphics, geographical maps, and information design. These tutorials provide clear, actionable instructions that make complex visualizations accessible.

Web and Print Design: Yau details how to create visuals suitable for various mediums, ensuring versatility in application whether for digital platforms or printed materials.

Personal Insights on Visualize This

Having taught data strategy and visualization for seven years, I find Visualize This to be an exceptional resource for a broad audience. Yau skillfully integrates scientific data visualization techniques with graphic design principles, providing practical advice along the way. The book’s toolkit is extensive, featuring R, Illustrator, XML, Python (with BeautifulSoup), JSON, and more, each with working code examples to demonstrate real-world applications.

The image shows an open page from the second edition of "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" by Nathan Yau. The page, from Chapter 5 titled "Visualizing Categories," features a colorful visualization titled "Cycle of Many," which depicts a 24-hour snapshot of daily activities based on data from the American Time Use Survey. This visual highlights how categories change over time and demonstrates the book's practical approach to data visualization.
Visualize This featuring a colorful 24-hour activity visualization based on data from the American Time Use Survey.

Even though I read the first edition years ago, I couldn’t put the second edition down all weekend. This book is a must-read for anyone who handles data or prepares data-based reports. Its beautiful presentation and careful consideration of every aspect—from typeface to page layout—make it a pleasure to read.

The book is user-friendly, offering a massive set of references and free tools for obtaining interesting datasets across various fields, from sports to politics to health. This breadth of resources is crucial for anyone looking to create impactful visualizations across different domains.

While the focus on Adobe Illustrator might be daunting due to its cost and learning curve, Yau’s examples show how Illustrator can enhance graphics created in other tools like SAS and R. I personally prefer the open-source Inkscape, but Yau’s insights helped me overcome my initial reluctance to use Illustrator, leading to more polished and professional visuals.

Yau uses R, Python, and Adobe Illustrator to demonstrate what can be achieved with imagination and creativity. Although some readers might desire more complex walkthroughs from raw data to final graphics, such material would require substantial foundational knowledge in R and Python. Including this would make the book significantly thicker and veer off from its focus on creating visually appealing graphics.

Conclusion: Visualize This is Essential Reading for Data Professionals

Visualize This (Amazon) is an indispensable guide for anyone serious about data visualization. Its methodical, data-first approach, combined with practical tutorials and a comprehensive toolkit, makes it a must-read for information designers, analysts, journalists, statisticians, and data scientists.

For those looking to refine their data visualization skills and create compelling, accurate graphics, this book offers invaluable insights and techniques.

Connect with me on LinkedIn and Twitter for more reviews and insights on the latest in data & AI, and #datamustread: