Data Points by Nathan Yue: Book review

Data Points: Visualization That Means Something by Nathan Yau
My rating: 4 of 5 stars

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.

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Beautiful LEGO by Boyle: Book review

Beautiful LEGO by Michael Doyle
My rating: 5 of 5 stars

This is a beautiful and inspiring book. The pictures are works of art. Some majestic in scale, some more at the level that you can imagine someone actually doing, if they had the artistic vision these creators did!

I like LEGO because of the ability to create. I remember as a young boy soon after a move using LEGO to build a model of my old house, with each floor, stairs, and every room represented. With my son, we have DUPLO's that have not gotten old. Some of the things we have made for him have been kept together for regular play for months at a time.

What this book and the artists that it interviews discusses is the use of LEGO as a medium for art. Where it makes the jump from a toy to art is when you creatively consider possibilities within the context of a constraint. And like the photographer who chooses to use black and white, the use of LEGO instead of clay is the fact that it comes in generally rectangular blocks. This book and the interviews recorded here deal with many ways of approaching this type of abstraction. From large scale recreations that rely on distance and scale to convert the blocks to a realistic looking objects, to abstractions with just enough detail so that the idea is recognizable and can fill in the rest.

My hope for my son is that LEGO gives him his first taste of creation, of having a picture in his mind and using building blocks, growing slowly towards that vision with all of the trial and error that involves, and ending with something he can call his own. And in this book, some of artists show that there start was much the same.

Disclaimer: I received a free electronic copy of this book through the Oreilly Press Blogger Program

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An Introduction to Data Science by Stanton: Book Review

An Introduction to Data Science by Jeffrey M. Stanton
My rating: 4 of 5 stars

This freely available book fills a nice little niche, people getting started in data analytics. The first problem people have in learning this is they tend to learn a number of techniques, but in practice they cannot get past the step of accessing and preparing the data. This book covers this and gives good practice in it. I plan on using this as the first of two texts for the course. This book will get them started (gently) in R and accessing and processing data, then a case based data mining book where they can use what they learn here to work with larger data sets that are then analyzed in the methods that the other book goes into more depth.

This book would be for those who are just getting started and need some hand holding as they get started with the R environment as well as those who don't really know where to get started with new data sources. It is most useful for those who are starting from nearly scratch, but also for those whose education included data analysis techniques, but whose training neglected those crucial steps on how to get started and why you should be using some class of method, not just the how. And since it is a free electronic book, it is a low cost way to get started.

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Data Science for Business by Provost and Fawcett: Book review

Data Science for Business: What you need to know about data mining and data-analytic thinking by Foster Provost
My rating: 5 of 5 stars

What Provost and Fawcett have done is to write a book on data mining that focuses on the why of data mining technique, which is great complement to all the books that focus on the how of data mining. And because it focuses on the why for the myriad of methods that fall under the heading of data mining, this would be a good source for a manager of a project for which data mining was merely part of the project, or for a source of good explanations when you need to explain to others what data mining methods (or buzzwards) can and cannot do.

I've come across a number of data mining books. Some are deep into the mathematics and statistics that underlie the methods of data mining. Others focus on how you implement methods. But while this helps with technique, a missing niche is the why, or the morality of data mining methods. They go over a range of methods, but the focus is on the task, recognizing what kinds of questions can be asked in a situation, then how to answer it. This is different from a methods book that has chapters focused on PCA, SVM, trees and forests, or other techniques. The second can lead to tossing out buzzwords. This book is the first, and is for having conversations about how to get a task done.

While I've read and worked through examples from books that focused on methods and implementations, I think that my understanding of data mining has improved significantly on reading this book. I'm recommending it to a former student who has since had to learn and implement these methods in practice, so he can better explain what he has done and its significance at his company. My only nit to pick is the title.  The book clearly focuses on data mining, not on other aspects of data science. Within that realm, I recommend it unreservedly.

Disclaimer:  I received a free electronic copy of this book through the OReilly Blogger program.

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Parenting Month 35: Oh the tales I have to tell

On your mark! Get set! Go!

We have an energetic child this past month.  Lots of running around everywhere.  Exploring outside.  Jumping off of things and just because.  And always having something to say. "I'm talking!"

There is the red circle

Along with all the activity comes a lot more talking. At home he is constantly talking. Sometimes he is talking to one of us. But he has started telling stories to stuffed animals Pooh and Turtle (he had not played with stuffed animals before), or even pretend to read (he has a book open, and he tells a story as he turns pages. The story may or may not be related to the book, but that does not really matter at this point, does it?) Day care reports that he is now talking to all of the staff, although not so much with his classmates. Although, day care also reported that he had a girlfriend (i.e. they hold hands and sit together for lunch) for a period of time (I don't know if this is current, not knowing the dating cycles of 2-year olds) The center staff also notes that he does not grab toys, but that is more because he never got attached to toys rather than being polite and playing well with others.

It turns out that most of his classmates at day care have older siblings, which means all of them share whatever is going around their older siblings schools. So he has another round of being sick with the ailment of the week. Of course, we have to report to the doctor that despite being sick, he is generally happy and playful, so this does not get all that much concern.

I'm hiking in the woods