Showing posts with label data visualization. Show all posts
Showing posts with label data visualization. Show all posts

6/05/2012

Visualizing Data: Exploring and Explaining Data with the Processing Environment Review

Visualizing Data: Exploring and Explaining Data with the Processing Environment
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This book allowed me to quickly create some simple applications using the processing API. So, in that respect, the book was successful. However, the book falls short in three respects.
1) One would expect a book with the title "Visualizing Data" to be crammed with pictures showing many different data visualizations. However, this book has relatively few. Every colleague of mine who passed by my desk and picked up the book had the exact same reaction.
2) The processing language is touted as a means for people unfamiliar with programming to get up to speed with visualization. However, I would be very surprised if anyone with little programming experience would get much out of this book.
3) Don't expect to use this book as a reference for the processing language. It is basically just a collection of half explained examples. Consider for example the function smooth(). This function appears in almost every example but forget about trying to find an explanation of what the function does in the book.
The book is probably worth buying to get up to speed quickly but plan on spending a significant amount of time sifting through the processing.org website and other online resources before being able to get anything non-trivial done. And if you don't already know Java then don't expect to accomplish anything even modestly complex without a lot of outside help.


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Enormous quantities of data go unused or underused today, simply because people can't visualize the quantities and relationships in it. Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:

The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact
How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details
Several example projects with the code to make them work
Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set
The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

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5/26/2012

Visualizing Data Review

Visualizing Data
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This book was recommended highly to me by a former university professor (and now consultant). It exceeds my expectations. The figures and acompanying explanations are very clear, as is the language throughout. Visualizing Data discusses several tools with which I was not familiar, and clarifies tools that I thought I understood (including box plots). I have taken several university statistics classes, but I believe this book would help anyone involved in displaying or interpreting data. A picture may be worth a thousand words, but when your business depends on it, a well-defined plot or graph can be worth much more. Visualizing Data enables you to produce well-defined plots and graphs with confidence.

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Visualizing Data is about visualizationtools that provide deep insight into thestructure of data. There are graphicaltools such as coplots, multiway dot plots,and the equal count algorithm. There arefitting tools such as loess and bisquarethat fit equations, nonparametric curves,and nonparametric surfaces to data.But the book is much more than just acompendium of useful tools. It conveys astrategy for data analysis that stressesthe use of visualization to thoroughlystudy the structure of data and to checkthe validity of statistical models fittedto data. The result of the tools and thestrategy is a vast increase in what you canlearn from your data. The book demonstratesthis by reanalyzing many data sets from thescientific literature, revealing missedeffects and inappropriate models fitted to data.

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4/19/2012

Interactive Data Visualization: Foundations, Techniques, and Applications Review

Interactive Data Visualization: Foundations, Techniques, and Applications
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Had to get this overpriced book for an Info Viz class. Be aware that this book is much more of a technical book than a design book. There's a ton of information contained in here, but I also found a surprising amount of quality issues.
First, the flow of the book seems completely off, diving into highly technical material in the second chapter, then pulling back into high level concepts in later chapters. Also, many of the images are not of the quality I would expect from a text book. Many are blurry or scaled inappropriately, given the amount of detail they contain. Finally, there are some glaring mistakes in the copy. For instance, at the end of one section of the book, placeholder notes from the authors of what should be written is included instead of the actual final copy! Where's the editor? Was it rushed to print?
Given the price, I expected a much higher level of quality. Despite the problems listed above, the text could be useful resource for anyone interested in the nuts and bolts of data visualization.

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This book provides the theory, practical details, and tools necessary for building visualizations or systems involving the visualization of data. The authors cover the spectrum of data visualizations, including mathematical and statistical graphs, cartography for displaying geographic information, two- and three-dimensional scientific displays, integrated analysis and visualization tools, and general information visualization techniques. Practitioners, developers, teachers and students as well as those interested in gaining some exposure to the field will get an in-depth understanding of visualization techniques and are provided with sufficient information, often with full source code, to complete an implementation; those with more modest aspirations can focus on the concepts, theory and high-level algorithm details.

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3/11/2012

Mixed Effects Models and Extensions in Ecology with R Review

Mixed Effects Models and Extensions in Ecology with R
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Many applications in ecology clearly are not amenable to use of the general linear model due to violations of its assumptions. In fact, in most projects I work on, things like correlation among the errors, nonconstant error variance, etc., are the rule, rather than the exception. If you are looking for an applied text dealing with these types of situations with lots of examples, and demonstrations on analysis in R, then you should get this book. It does not delve into theory; there are plenty of other textbooks where you can fill in those details if you are interested. Rather, this book would be ideally suited for quantitative ecologists, biometricians, and statistical consultants who work in life sciences. Another nice thing is that the book does not assume you are an "R expert". Well done.

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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

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