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Data Points: Visualization That Means Something
A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Visualization That Means Something , author Nathan Yau presents an intriguing complement to his bestseller Visualize This , this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Create visualizations that register at all levels, with Data Visualization That Means Something .
320 pages, Paperback
First published March 1, 2013
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Displaying 1 - 30 of 43 reviews
April 17, 2014
This book is a good summary and overview of the current state of data visualization. However, it is not as good as many others books on the topic: Tufte is my pick for the best (The Visual Display of Quantitative Information), and his book is much better produced than Data Points. Data Points often has illegible plots, blurry printing on some figures, an inconvenient choice of book binding for dual-page printing, and a rushed feeling when it comes to organization, layout, and narrative. Tufte, by contrast, clearly spent ages laying out every page and even admits to adjusting his phrasing so that his sentences lined up nicely on the printed page.
There is also very little how-to; if you know a bit about data viz, you may with to skim Stephen Few's books (Intelligent Dashboard Design is good, especially the last chapter, and is nearly as well-produced as Tufte's books). Yau's work seems more intended for a web-centric audience, and would likely be better served as an online resource (which it may well be) or an interactive e-book.
Data Points' main strength lies in its visual breadth. There are a lot of examples here - mostly of Tableau or D3.js visualizations. It's great to skim through to pick out the best of the bunch, such as the Visual Cues on page 144-145 or the wind map comparison on p 249-250. (Note that these are not necessarily Yau's work!) Chapter 5, "Visualizing With Clarity," is probably the strongest chapter in the book, with very well-focused examples. If you've got some experience in this field, you might consider starting there and working backwards to see what Yau has to offer.
There is also very little how-to; if you know a bit about data viz, you may with to skim Stephen Few's books (Intelligent Dashboard Design is good, especially the last chapter, and is nearly as well-produced as Tufte's books). Yau's work seems more intended for a web-centric audience, and would likely be better served as an online resource (which it may well be) or an interactive e-book.
Data Points' main strength lies in its visual breadth. There are a lot of examples here - mostly of Tableau or D3.js visualizations. It's great to skim through to pick out the best of the bunch, such as the Visual Cues on page 144-145 or the wind map comparison on p 249-250. (Note that these are not necessarily Yau's work!) Chapter 5, "Visualizing With Clarity," is probably the strongest chapter in the book, with very well-focused examples. If you've got some experience in this field, you might consider starting there and working backwards to see what Yau has to offer.
March 23, 2021
I give this book 3.5 stars. The first few chapters cover basic concepts like what type of graphs to use for different data (proficient Excel users would know this already). Some of the guidance on creating visualizations was useful, some not (especially where the visualizations were for large data sets and the small print was hard to read).
June 18, 2015
Yes, as commenters have alluded to, some of the charts are a little small. But this book 100% enthralled me from beginning to end. It is so well organized and provides exactly the sort of foundation I was looking for to data visualization. I've already started noticing new sorts of things when I look at infographics.
Data Points gives a good overview of the "spectrum that stretches from statistical graphics to data art," and provides a lot of useful generalized advice. I plan to read Visualize This next since it seems to be more hands-on and in-depth.
Data Points gives a good overview of the "spectrum that stretches from statistical graphics to data art," and provides a lot of useful generalized advice. I plan to read Visualize This next since it seems to be more hands-on and in-depth.
November 5, 2013
Provided some useful skills to apply and ways to think about visualization. I learned a fair amount.
February 18, 2018
A couple of years ago, I was looking for inspiration in the field of data visualisation and I bought a handful of books. Unfortunately, I picked up the other books first. It's clear now that I should have read this and Yau's sister (and earlier) book "Visualize This" first, as they provide a solid fundamental.
I read Data Points after Visualize This, which worked well, although you could in theory read them in parallel. Confusingly, Data Points deals more with visual aspects, and Visualize This deals more with data. I suppose they struggled to find as catchy and relevant a name for his follow-up publication.
Bearing in mind these are starter books (dealing with high-level, broad-brush points, while also diving into basic nuts-and-bolts aspects) I reckon both books deserve 4 stars based on their readability and structural clarity. I would have liked to have dug deeper and got funkier, but hey, that's for another book...
Negatives? Yau's writing style is poor. In the end, I had to picture him talking to me, to move through the text - it didn't work in my voice. Far too many grammatical errors, typos and such like. Data Points starts off better in this respect, but gets sloppy too. Failure to distinguish between singular and plural is common (and I'm not just talking about the word "data" here). Ironically, since he actually cites this in his own guidance as a point of concern, the second book suffers especially from a spatial disconnect between graphics and the text where they are referenced (sometimes 4 pages apart!). Understandable on an occasional basis, this gets tiresome when it becomes the norm. In some cases, full-page graphics are necessary, and often aesthetics point to greater space usage by visual components (some are really beautiful, some really complex), but you get the feeling that there was also an incentive to fill out the pages, this book being somewhat shorter than its predecessor (which filled many pages with lines of coding examples, and rightly so.)
In the end, Yau writes in a very homey, personal way, which is quite engaging and is a relief from the designer arrogance/fluffiness or data/stats over-technicality which you might fear from a book in this field.
I read Data Points after Visualize This, which worked well, although you could in theory read them in parallel. Confusingly, Data Points deals more with visual aspects, and Visualize This deals more with data. I suppose they struggled to find as catchy and relevant a name for his follow-up publication.
Bearing in mind these are starter books (dealing with high-level, broad-brush points, while also diving into basic nuts-and-bolts aspects) I reckon both books deserve 4 stars based on their readability and structural clarity. I would have liked to have dug deeper and got funkier, but hey, that's for another book...
Negatives? Yau's writing style is poor. In the end, I had to picture him talking to me, to move through the text - it didn't work in my voice. Far too many grammatical errors, typos and such like. Data Points starts off better in this respect, but gets sloppy too. Failure to distinguish between singular and plural is common (and I'm not just talking about the word "data" here). Ironically, since he actually cites this in his own guidance as a point of concern, the second book suffers especially from a spatial disconnect between graphics and the text where they are referenced (sometimes 4 pages apart!). Understandable on an occasional basis, this gets tiresome when it becomes the norm. In some cases, full-page graphics are necessary, and often aesthetics point to greater space usage by visual components (some are really beautiful, some really complex), but you get the feeling that there was also an incentive to fill out the pages, this book being somewhat shorter than its predecessor (which filled many pages with lines of coding examples, and rightly so.)
In the end, Yau writes in a very homey, personal way, which is quite engaging and is a relief from the designer arrogance/fluffiness or data/stats over-technicality which you might fear from a book in this field.
July 9, 2018
TODO full review:
+++ Surprisingly good! This book reads like a modern review of great projects in information visualization, plus a summary of best practices in the field. In other words, it's Manuel Lima meets the group of excellent teachers Stephen Few, William S. Cleveland, and Edward R. Tufte. Well worth reading.
+++ I enjoyed the rich set of projects covered in this book.
+++ I could see how my team would benefit from reading the dos and don'ts described in this book, albeit, concisely. I found it a strength of this book that it manages to avoid the snarky remarks of Stephen Few and the plain disparaging remarks of Edward R. Tufte (the latter, epitome of hubris).
+++ Surprisingly good! This book reads like a modern review of great projects in information visualization, plus a summary of best practices in the field. In other words, it's Manuel Lima meets the group of excellent teachers Stephen Few, William S. Cleveland, and Edward R. Tufte. Well worth reading.
+++ I enjoyed the rich set of projects covered in this book.
+++ I could see how my team would benefit from reading the dos and don'ts described in this book, albeit, concisely. I found it a strength of this book that it manages to avoid the snarky remarks of Stephen Few and the plain disparaging remarks of Edward R. Tufte (the latter, epitome of hubris).
November 11, 2022
I read this year "Storytelling with Data" by Cole Nussbaumer Knafli, and found it quite insightful. I apply many of the principles from that book on my day to day job, and has changed by perspective on how I think about data
This books provides lots of insightful tips about visualizations, but focuses more on the artful aspect of visualization. Yes, it provides many common visualization tips, however, it also tells us that visualization can be more meaningful if e sometimes break some of the schemes we already know. It's like writing. Yes, you need to know about spelling and grammar, but there are tons of different writing styles, and there´s not a single writing style that is the best. Context is key.
Anyway, it was fast read. Recommended. I´d just love that the book followed some of it´s own tips, like making charts bigger so they can be more readable :(
This books provides lots of insightful tips about visualizations, but focuses more on the artful aspect of visualization. Yes, it provides many common visualization tips, however, it also tells us that visualization can be more meaningful if e sometimes break some of the schemes we already know. It's like writing. Yes, you need to know about spelling and grammar, but there are tons of different writing styles, and there´s not a single writing style that is the best. Context is key.
Anyway, it was fast read. Recommended. I´d just love that the book followed some of it´s own tips, like making charts bigger so they can be more readable :(
March 13, 2023
This is a very generic book on visualization that fails to address its main point. "It was OK" (the meaning of 2 stars in Goodreads) as a review on the subject, but I don't think people who are interested enough in visualization to read this book will learn anything new.
The author seems to be afraid in diving really deep in the topics covered (such as the meaning of colors and how to select typefaces). I constantly got the impression that Yau was just dumping some ideas without getting back to literature sources that you make for a better read.
This is meant as a sequel to Visualize This, but does not add anything to it.
The author seems to be afraid in diving really deep in the topics covered (such as the meaning of colors and how to select typefaces). I constantly got the impression that Yau was just dumping some ideas without getting back to literature sources that you make for a better read.
This is meant as a sequel to Visualize This, but does not add anything to it.
Read
April 7, 2021A decent introduction to visualization. As a working data science person, a lot of the content was a little obvious -- I found myself skimming more and more as I went through the book. The auther tends to fill pages with middling analogies and vague advice. The most valuable part of the book are the visualizations themselves. The elements of visualization are explained well, but are buried in 200 pages of other stuff.
April 1, 2022
Good solid introduction to the art of data visualization. Lots of nice examples and exploration of the various questions a data designer should ask themselves. It's an introductory book, I felt I knew most of what I was reading already (and I'm no expert). But it's not a hands-on book in any way, it's something you passively read and learn from. So for someone learning visualization this needs to be paired with implementation exercises.
September 21, 2020
Great for Beginners Getting into Visualization!
This book is great for beginners looking to start and improve their data visualization. This book does a good job of walking through the basics of what needs to be considered from an analysis perspective and design perspective. It’s not prescriptive, but does a good enough job to improve ones understanding of the topic.
This book is great for beginners looking to start and improve their data visualization. This book does a good job of walking through the basics of what needs to be considered from an analysis perspective and design perspective. It’s not prescriptive, but does a good enough job to improve ones understanding of the topic.
March 20, 2018
This book has given a foundation of data points, as the name suggests. This book is for someone who has started making visuals (or planning to) and looking for the mindset one needs while thinking about what visual to make. Nice book on visual thinking.
December 22, 2018
Decent book about visualization, but nothing groundbreaking. Seems a little bit dated, but that’s the nature of this field. My biggest gripe is how the examples are on following pages, so you read about the visual before seeing it. There were also a few obvious editing errors.
May 8, 2017
It's a good collection of visualization examples and some practical comments though no strong process or theories explained.
January 12, 2019
Solid overview but not much in terms of new or thought-provoking content.
September 18, 2019
Good introductory text to data visualization. Good balance of explanations and examples.
Read
February 28, 2023Data Points: Visualization That Means Something by Nathan Yau (2013)
November 25, 2023
Good beginners guide about Data Visualization
This is a good book for beginners, who are interested about understanding data and ways to present it to an audience.
This is a good book for beginners, who are interested about understanding data and ways to present it to an audience.
October 27, 2013
This book is not about how to create data visualizations, it is about how you use visualizations to communicate data. In that respect it is not trying to be a book about tools, but a book on aesthetics, it focuses on how you evaluate different combinations of visualization options for communicating different types of information about data, not just a number of rules. In this respect, it goes considerably deeper and profound about how people comprehend and interpret visualizations than a set of pithy rules masquerading as common sense. In this respect, it is a successor to Tufte in an age where being able to try alternative visualizations and even having consumers interact with the visualizations is cheap.
The book is not a description of various types of visualizations, even though it has such descriptions and discussion of comparative assessments. It is a book on how to think about the message(s) you are trying to communicate, and how to do so in ways that can engage the reader at many layers of depth where simple messages are easily grasped, and complex messages can be absorbed with their relations and implications. Along the way he discusses the relative strengths of using different types of visual cues to communicate information (position, length, angle, direction, shapes, area, volume, saturation, hue), which is much deeper than saying 'bar charts are better than pie charts' (which is an argument that a post-doc tried to engage in with me once). After a brief introduction, he proceeds to show you by example after example of the comparative qualities of each cue, and also how they can be used in combination to show multiple levels of information and relationships.
One of my biggest insights from 'Data Points' is actually not discussed in the book. The book gives you the understanding you need to evaluate the range of combinations of means of presenting data. But about halfway through I realized that the discussion and philosophy of combining these visualizations has a name. Wilkinson's Grammer of Graphics. I have learned the ggplot implementation of Grammer of Graphics, and I favor it above other plotting families in R and Python as being more flexible and giving you more control over the result. The discussion in Data Points explains why Grammer of Graphics is important, it provides an interface for exploring combinations of aesthetics (visual cues) to communicate aspects of complex data sets. And with this, it will probably change how I present and teach visualization for data analysis.
The book is not a description of various types of visualizations, even though it has such descriptions and discussion of comparative assessments. It is a book on how to think about the message(s) you are trying to communicate, and how to do so in ways that can engage the reader at many layers of depth where simple messages are easily grasped, and complex messages can be absorbed with their relations and implications. Along the way he discusses the relative strengths of using different types of visual cues to communicate information (position, length, angle, direction, shapes, area, volume, saturation, hue), which is much deeper than saying 'bar charts are better than pie charts' (which is an argument that a post-doc tried to engage in with me once). After a brief introduction, he proceeds to show you by example after example of the comparative qualities of each cue, and also how they can be used in combination to show multiple levels of information and relationships.
One of my biggest insights from 'Data Points' is actually not discussed in the book. The book gives you the understanding you need to evaluate the range of combinations of means of presenting data. But about halfway through I realized that the discussion and philosophy of combining these visualizations has a name. Wilkinson's Grammer of Graphics. I have learned the ggplot implementation of Grammer of Graphics, and I favor it above other plotting families in R and Python as being more flexible and giving you more control over the result. The discussion in Data Points explains why Grammer of Graphics is important, it provides an interface for exploring combinations of aesthetics (visual cues) to communicate aspects of complex data sets. And with this, it will probably change how I present and teach visualization for data analysis.
September 23, 2013
Not what I expected, but glad I read it. The review that I had read of this book suggested that it would explain the big deal about "big data." Big data is one of the latest twists on information technology, where, as I understand it, smart IT folks can mine huge data bases and find patterns, hidden trends, useful trees in massive forests. If that's what you're looking for, this book isn't it. Instead, Yau emphasizes the visualization aspects of presenting information. He shows the myriad ways that reams of data can be presented so that they convey a point or conclusion that normal people can visualize - and feel the impact. Think pie charts, scatter plots, etc., on steroids. He includes some amazingly creative, smart, and unusual ways to express complicated data. The artistry of these "graphs" is stunning. In one example, he showed a "history flow" graph that depicted the evolution of the Wikipedia article on Chocolate - it's a work of art!
If your work depends on presenting info in clear and compelling ways, you need this book on your bookshelf. (The book is less than 50% text - the rest is real examples of figures, graphs, pictorial representations of various large data bases.)
If your work depends on presenting info in clear and compelling ways, you need this book on your bookshelf. (The book is less than 50% text - the rest is real examples of figures, graphs, pictorial representations of various large data bases.)
July 18, 2013
There's so much overlap in all these recent data visualization books (Alberto Cairo, Stephen Few, Stephen Kosslyn, etc.) that I have trouble remembering exactly what's distinct about this one vs the others. There are also a few confusing typos that change the meaning of his (otherwise good) examples.
But it's a worthwhile quick read if datavis is an area of interest to you.
And I do like the examples, such as the use of histograms as colorbars on a choropleth on p.275.
Chapter 5 provides particularly nice advice on fonts/typography, annotations, data transformations, etc.
Chapter 6 includes a handy chart relating questions (e.g., "What ___ is the best and worst?") to corresponding statistical concepts (max and min) and possible visuals (bar chart).
I also like Yau's suggestion on p.264 to consider relatability ("...let readers see the data as it pertains to them... When you note the overall trend, you most likely looked at your own age and gender to find the corresponding probability...").
Note to self: read the "Making Sense of Graphs" journal article, "for a good summary of how students comprehend graphs."
But it's a worthwhile quick read if datavis is an area of interest to you.
And I do like the examples, such as the use of histograms as colorbars on a choropleth on p.275.
Chapter 5 provides particularly nice advice on fonts/typography, annotations, data transformations, etc.
Chapter 6 includes a handy chart relating questions (e.g., "What ___ is the best and worst?") to corresponding statistical concepts (max and min) and possible visuals (bar chart).
I also like Yau's suggestion on p.264 to consider relatability ("...let readers see the data as it pertains to them... When you note the overall trend, you most likely looked at your own age and gender to find the corresponding probability...").
Note to self: read the "Making Sense of Graphs" journal article, "for a good summary of how students comprehend graphs."
July 24, 2013
Nathan Yau is the heir to Edward Tufte when it comes to data visualization and graphics so anyone who is serious about that field needs to read his work. This is the newest one; it builds on earlier work of course but there is some overlap. He begins to provide some basic instruction in statistics but only the most basic. Most of the message is explore and get to know your data through a wide variety of charts. Physically, the book and graphs are beautiful; there are some really beautiful graphs here (wind map, for one). So, nothing earth shaking but helpful around the margins. I am going to go try out some star charts tomorrow, for instance. Most people would probably get something from the book.
July 28, 2013
This is a beautifully produced book with lots of great ideas for visualizing data. I personally thought the balance was a little off--there's almost nothing about how to actually create some of these quite complex graphics (I think there's another book by the same author that goes deeper into this). It's also a little light on the analytic and exploratory aspects of graphics as opposed to the communicative and visual aspects. But the charts showing how to compose graphics from the basic components are worth the price of the book. I would also highly recommend Bertin's Semiology of Graphics, which does much the same thing but with a much more comprehensive theory underlying the choices.
September 12, 2013
This book is a good-enough collection of data viz common sense. The glossy print and vast collection of interesting charts are nice(however I was disappointed that the xkcd radiation chart did not get a mention).
The fact that the author did not appear to follow his own advice regarding flow of narrative was ironic. The book had me flipping forward multiple times to see the chart that was being talked about. The discussion about tools was too brief and left me wanting.
All in all its a good-enough primer for anyone interested in visualization.
The fact that the author did not appear to follow his own advice regarding flow of narrative was ironic. The book had me flipping forward multiple times to see the chart that was being talked about. The discussion about tools was too brief and left me wanting.
All in all its a good-enough primer for anyone interested in visualization.
February 6, 2017
A great resource and textbook for learning how to analyze data and how to design visualizations to help communicate what the data is trying to say. The writing has a great voice, which made it entertaining to read. And all the examples were inspiring, intriguing, and insightful.
It's pretty difficult to fully finish reading a textbook, but I did my best. I got the information I needed from it and that's good enough for me. Not to mention, I took a class on the fundamentals that Nathan Yau discuss, so much of this book was review for me.
It's pretty difficult to fully finish reading a textbook, but I did my best. I got the information I needed from it and that's good enough for me. Not to mention, I took a class on the fundamentals that Nathan Yau discuss, so much of this book was review for me.
February 18, 2014
I think this leans Edward Tufte, but with a more statistic type mindset and a little less art driven. The book is also split into sections a bit better. It's mostly focused on how to interpret data and visualize it. Part of two books I've purchased, the other one being visualize this. It lacked some organization issues and felt like I occasionally was reading the same thing. Overall, not a bad read, though a lot of overlap with Edward Tufte and Stephen Few.
August 31, 2013
I agree with the author maybe 50 per cent of the time. Many of the examples aren't my cup of tea. Notwithstanding the above, the book is interesting and puts forward a mostly common sense approach.. I wouldn't rely on it wholly but think the book wd complement the current collection of texts on the topic.
April 29, 2016
Nice exploration of and discussion around the process of creating data visualizations. While there's nothing super mind-blowing in the book, I appreciated the willingness to talk through the thought process and resulting effects of different variations on the same basic visualization. The discussion, even around really basic fundamentals, contained some thoughtful and nuanced observations.
May 14, 2014
Great book about the fundamentals of data visualization. Even for seasoned data visualization artists, there are several useful ideas about how to think about data visualization and infographics.
"The key to high quality data art, like any visualization, is still to let the data guide."
"The key to high quality data art, like any visualization, is still to let the data guide."
June 17, 2014
A good, solid, overview of creating meaningful visualizations. There is much more detail in books like "Grammar of Graphics", "Semiology of Graphics", or any of Tufte's work, but this one pulls the basics together in one place, with good looking examples.
Displaying 1 - 30 of 43 reviews