Nathan Yau

Data Points reads like a friendly textbook engaged with visualization. It is less concerned with tips and tricks, and more concerned with understanding. For instance, on the neverending debate on pie charts, Yau pleads neutrality. He sees that pie charts have
their place, albeit a limited one, in the visualization domain. As such, he promotes freedom and the ability to choose above all.


There are lots of data that need appropriate visualization in today's world. The ability to procure data on show more just about anything has gone up astronomically in recent years. Yau's approach does not offer a lot towards programmers who want to mass-produce visualizations. Instead, this author of FolowingData.com focuses on building one powerful, elegant visualization at a time. He's less an artist and more a statistician (belying his PhD in Statistics). He focuses on communicating the right message through visualization of your data.

As is customary in books like this, the examples tend to carry the narrative. While the communication of the principles of visualization is the primary message of this book, Yau carries his story through interesting examples of how people communicate (and sometimes miscommunicate) with data. While not as erudite and varied as Tufte's compilations, Yau's work provides much food for thought as the reader analyzes the graphics. Reading this book is simply fun. show less

What sets this book apart from most other books on data visualization (and there are quite a few!) is that Nathan Yau focuses strongly on the craft skills of making visualizations. He presents a range of tools and techniques that are, if not timeless, then at least reasonably useful and relevant even seven years after initial publication. The instructions on how to create effective visualizations are structured according to visualization purposes -- patterns, relationships, differences and show more so on -- and consistently delivered in a way that inspires own experimentation. show less

Following on from the success of Visualize This, the author aims to extend the scope of his topic to a more general take on data visualization. Different phases of the work process are covered and the book is more independent of specific tools and software. However, many of the examples and main ideas are the same as in the previous book, and it sometimes feels more like a revision that a sequel.

This is a great primer on what data visualization is and how the author goes about it. For a visualization to mean something, it must speak to the audience. If that happens, the visualization is successful; it is not dogmatic. This is also not a step-by-step guide to using a particular vis package (for that see, e.g., Yau's "Visualize This.") It is a call for mindfulness in creating a visualization.