I started to dig into visualization about a year ago. The topic is fascinating; visualization can be used in a wide variety of applications, including agile software development. I’ve read several books related to information visualization, and I’d like to share my reading experiences. Hope this list will be helpful to someone.
by Jacques Bertin
This is a remarkable book. It provides a very clean and beautiful theory of data visualization that you can apply to practice immediately. Yes, the book is quite old, but absolutely relevant and has a fresh feeling about it. Bertin and Cleveland are great thinkers, as they’ve come up with some breakthrough data visualization concepts.
The most efficient constructions are those in which any question, whatever its type or level, can be answered in a single instant of perception, in A SINGLE IMAGE.
by Nathan Yau
This book was far from being at the top of my reading list. Maybe that is why I find it quite dull: very simplistic, useless information on data mining, etc. Maybe it can pass as someone’s first book on the subject, but I think that if you’ve previously read at least one book on information visualization, this one can be skipped without hesitation.
I have to admit. Data checking is definitely my least favorite part of graph-making. [...] But this is what good graph designers do.
by David Sibbet
The main thing that I’ve learned from this book is: you’d better visualize everything. Yes, everything you say. When you explain your idea, work to support it with some visuals. When you discuss a solution – sketch it. When you create a roadmap or a plan – draw it. Visuals are great helpers and communicators. Even one picture can spark a good discussion and bring new ideas.
Visual language … born of people’s need, worldwide, to deal with complex ideas that are difficult to express in text alone.
by Stephen Few
A really disappointing book. Examples are so awful that I can’t even look at them. The information continuously repeats itself. I haven’t learned anything new from the first part of the book and just skimmed the last chapters. Anyway, this book is severely outdated.
Dashboards almost always require fairly high-level information to support the viewer’s need for a quick overview.
by Edward R. Tufte
The famous Tufte books. I can give just one advice: read them all. You should savor the aesthetics of their design, typography and great illustrations. These books are crafted with care. I love crafted things. The content is great as well. Tufte’s work is based on Bertin’s teachings, but it has some novelty and passion.
This book is all about how to visualize things. It has examples from various domains.
There’s a book that you simply must see. Riveting ideas on how to tell compelling stories of cause and effect using numbers and images.
by Edward R. Tufte
How to display various types of information? This book is not about just numbers, but about many things.
If the numbers are boring, then you’ve got the wrong numbers.
Clutter and confusion are failures of design, not attributes of information
by Edward R. Tufte
This book gives a great start for any data-specific visualizations. Data-to-ink ratio and sparklines are novelties invented by Tufte. I think this book is the most useful one of all Tufte’s books.
Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose.
by Colin Ware
This book gives answers to how, why, and what we see. Visual perception, encoding channels, color, memory — these things may sound pretty advanced. This book can hardly be considered a straight help reference on data visualization, but it provides a very good basis for understanding various techniques, and understanding is priceless.
If we understand the world through just-in-time visual queries, the goal of information design must be to design displays so that visual queries are processed both rapidly and correctly for every important cognitive task the display is intended to support.
by Bill Buxton
I purchased this book at the UXLX conference in Lisbon. I did not expect too much of it initially. But after several dozen pages it paid off every cent I’d spent and exceeded my expectations in every possible way. This book is for UX designers, yes, but I’d say every executive should read it. There’re so many gems inside.
Well, this book gives no specific frameworks. It’s just extremely inspiring. Read a complete review.
Without appropriate design, yesterday’s success is tomorrow’s failure, since today’s great applications are tomorrow’s legacy systems
by William S. Cleveland
Cleveland’s books are not an easy read. They are full of terms and even include some maths. Still they are a must-read for any serious data visualizer. Cleveland has described many useful principles. The book is very practical.
It is hard to imagine anyone reading this book and not getting some good ideas to put immediately into practice.
by William S. Cleveland
Quite many concepts in this book have already been described in “The Elements of Graphing Data”. This one is more advanced and has more maths inside. I recommend to start with “The Elements…” and then grab this book to learn about the quantiles, Q-Q plots, jittering, coplots and dot plots.
We need more than just the logarithm in our arsenal of transformation methods. Logs cannot be used for data with zeros unless the data are adjusted. And a transformation other than the logarithm is often the one that leads to a simple data structure; in many cases the logarithm can fail to cure scewness and monotone spread, but another transformation does so.
The book ends the same way it begins, with dry data.
by Wolfgang Aigner, Silvia Miksch, Heidrun Schumann and Christian Tominski
This could be the first book that aggregates all the body of knowledge on visualizing time series: good theory, historical excursus and many real examples. The authors have categorized time-oriented data visualization patterns, and this is a major – and very practical – achievement. The book has no examples, though (it’s quite small for that type of information).
As visualization researchers, we are intrigued by the question of how this important dimension can be represented visually in order to help people understand the temporal trends, correlations, and patterns that lie hidden in data.
Which books on visualization have you read? Can you share your experiences?