Data Points: Visualization That Means Something

Bought this book for my son who requested it. He is a game designer and uses data to discern information about players. He also uses data to present ideas to clients and his boss, co-workers, etc. so wanted something with ideas on different ways to do that. Primarily, he uses data to make other people care about what he's found, and make the data easier to digest, he says.He found helpful ideas in this book. The graphics are wonderful, quality of the book visually is excellent. The author has a PhD in statistics from UCLA and has a site at FlowingData. Some of the content in this book includes: *discover what data is and what you can learn from it *learn how to explore your data, find the story, and bring it to life *understand visualization that lets you present and express meaning in data *tap into your creative side and determine the most effective way to tell your story *compare tools for exploration and analysis *allow data, the story, and your goals to dictate visualization techniques with geometry, charts, maps, color, art and humor.This book was helpful for his needs, and he is pleased with it.

The review trends of Yau's last book have already started with this edition: "too basic." Maybe we could graph the stats of those reviews, then look at the very topmost band of readers to find the "perfect" audience, vs. the large body of outliers who will trash this as oversimplistic. So, get into alpha, and visualize a bell curve, with "perfect for me" on Y and age/experience on x:DON'T BUY IF:--You're in the heavily skewed, lightly shaded, experienced right side of the curve, with even good basic experience in data presentation. I'd include any mid level manager who has decent powerpoints in this group. The colorful pictures are gorgeous, as in  Visualize This: The FlowingData Guide to Design, Visualization, and Statistics , and if you have a LOT of disposable income, you "could" buy it just for the pictorial ideas (paper is coated matte, images are 4 color, very high quality book production wise). If you're a post undergrad freshman, you might find the advice too basic. There also are a lot of discussions of data "types" but very little about psych. For example, starting a presentation with the statment "My purpose here is to INFORM" often gets audience hackles down if they're resistant to being sold or convinced-- not much of that is covered here.--You're a graphic artist or graphic pro, unless, again, you're just looking for pictorial and presentation ideas, and not advice (the illustrations, as in the last edition, are stunning).BUY IF:--You're very new to data presentation and aren't even sure whether red goes with green or tables are better than scatters in a given situation.--You're, again, looking for VISUAL ideas to supercharge your presentations, NOT programming tips or even English advice on details. ONE EXCEPTION to this volume compared to Yau's last book: there ARE a good number of example visuals by artists other than Yau (although his are still astonishing), and in THOSE CASES, the author does give the website. In some cases, these are just bigger online pictures of the graphics, in others, there actually is an explanation of the techniques.Now, for the good stuff. If you KNOW that this book is NOT for pros, you won't buy it, then downstar it because you're disappointed. JUST DON'T WASTE YOUR MONEY if you are looking for comparisons between R and visual basic, steps on translating LaTex and PostScript to .jpg, etc. The level of technical advice amounts to: "R is being used by more and more researchers and statisticians" (and that not until p. 283 of 290). There ARE a number of examples of open source and other software like indiemapper, GeoCommons, ArcGIS, Gephi, Imageplot, Treemap, Tilemill, etc. but the author only mentions them, and leaves you the autodidactic task of figuring out, for example, which do and don't work with Python, RSS, PHP, HTML5 and other pertinent questions pros would ask. But think about this: if you ARE very new to presentation, these tips WILL be eye openers and of great value, as you could surf for hours and not be able to compare or value what's worth it and not. At least beginners get a head start on what this very experienced statistician and author USED throughout the book.The biggest problem I saw with previous reviews is that the purchasers seemed to expect detailed explanations of how the author created the stunning graphics. This is NOT that book. The software is still not always mentioned with each visual, and steps are really NEVER given that detail "how to" get that effect, let alone scripting, code, or even pseudocode. The book is truly more of a coffee table text showing best practices, as an artist would, but not giving a tutorial on techniques. I know you've watched some tutorials on YouTube that are really "show off" steps by the programmer, with no real intention to show you how to do it. This isn't that bad, as it does have many important "rules of thumb," especially on mistakes to avoid if you're a novice.So, people who say this is a must buy, or people who say this is a waste of money are both wrong. The solution to that axis of opinion is an intesecting plane visual-- if you're relatively new, don't expect technical detail, and love to get visual ideas and inspiration, you won't go wrong with this volume. If you're expecting to learn tricks and tips in R vs. Excel, get dashboard and data texts on those specific programs instead, and you'll be much happier. Expect a lot of beauty, but not how to get there!!!Library Picks reviews only for the benefit of Amazon shoppers and has nothing to do with Amazon, the authors, manufacturers or publishers of the items we review. We always buy the items we review for the sake of objectivity, and although we search for gems, are not shy about trashing an item if it's a waste of time or money for Amazon shoppers. If the reviewer identifies herself, her job or her field, it is only as a point of reference to help you gauge the background and any biases.

Enjoyed the book with several interesting illustrations, Good pictures and graphics that demonstrate the value addition from data visualization. Summary chapter has useful pointers for practitioners.

I'm responsible for managing requirements, vetting source data, and for enabling content authors to ingest the data subsets in our department's analytics platform using Tibco Spotfire server and Pro client. Because data visualization (DV) delivery is a very new territory for us, this book has been extremely helpful in terms of its many fine examples along with the detailed stories behind each.Yau's book is helping me create design standards for our organization. I also use it as a content source for our analytics web portal landing page to help us communicate, explain, manage expectations, and set the tone in guiding our content authors who are developing new DV applications.It's a great book to show around the department and it helps get our developer's creative juices flowing. Well worth the money invested, and highly recommended reading.

I bought the book this past weekend and already finished reading it (kindle version), so here i share my comments: 1. The book is easy to read, it is non-technical which is warned in the overview. 2. As an experienced data analyst, most of the time you approach to a problem following a top-down structure which is done through some examples in the book but it remains superficial. When presenting to an audience, the author mentions "context" as a fundamental element of data analysis. In my personal experience this is achieved through a top-down approach. 3. I think the book could be more case-based, start with some question, then explore the data from different perspectives while constructing the visualization step by step (without involving technical details or specific tools). 4. In general, the book is a series of hints that you need to be aware when visualizing information effectively.When comparing it with Beautiful Visualization by Julia Steele whose approach is visualization through examples, the Steele book describes the whole process in each case, sometimes including source code for replicating the process. It is clear that the purpose of each book is different but it could had been better.In conclusion, excellent book if you are inexperienced in data analysis or if you have a non-technical background such as marketing, commercial or social sciences.

Très bon contenu.La visualisation avec une vue différente de la plupart des autres livres sur le même sujet.Le seul point négatif est la faible qualité de la reliure et de la matière du livre.

Excellent introduction to the concepts behind visualization, good for neophytes as well as those with a statistics background that have no experience in visualization. However, the connection between visualization and statistical analysis should be covered more deeply.

Lo consiglio a chi vuole trovare spunti efficaci ed assolutamente "d'avanguardia" sul data management e la coesione tra input ed output

I bought this on checking other reviews as I'm interested in studying data visualisation techniques. What a great, insightful book. Strikes the right balance between technical help, reference guide and inspiration creator...Also would recommend 'Information is Beautiful' by David McCandless, a great example of a good data vis. practitioner.

Le livre est inspirant mais ULTRA basique. Si vous connaissez son blog, vous y trouverez rien de neuf. Il s'adresse aux personnes qui ont l'habitude de travailler avec des graphiques basiques et on envie de se faire une idée sur les alternatives plus sexy (i.e. qui suscitent les émotions et la curiosité).Pour ma part se fut une lecture plaisante et parfois amusante mais où j'ai très peu appris.... J'attendais beaucoup plus de cette lecture....