- Paperback: 592 pages
- Publisher: O'Reilly Media; 1 edition (July 22, 2016)
- Language: English
- ISBN-10: 1491920513
- ISBN-13: 978-1491920510
- Product Dimensions: 5.9 x 1.1 x 8.9 inches
- Shipping Weight: 2.1 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #187,576 in Books (See Top 100 in Books)
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I think of this book as a general guide to how to navigate the modern landscape of web-based data visualization, a broad map for a pretty confusing landscape. I don't know of any other book that does that, even if you do have to delve into other resources on the side as you go.
Throughout the book there is an example which uses data about Nobel Prize winners. The data is pulled from Wikipedia, cleaned, analyzed, hosted, and visualized. Each step of the process was explained well.
Language in the book is better than most as the author takes times to explain in interesting ways. On page 274 when introducting different graph types he says: "The humble bar chart is a staple for a lot of visual data exploration." What a nice way to explain the importance of the bar chart.
At the end I had a clear picture of how to build an impressive data visualization website form scratch. It was great to see an author who can provide a full-stack implementation and explain each aspect with ease.
This book is in color which I really appreciated. It helped a lot to have syntax highlighting and I hope O'rielly does this for all their other books. The dimensions of the book is smaller than other O'rielly books as well; it was comfortable.
The first sections cover data procurement, cleaning techniques, and exploratory data visualization with pandas and matplotlib. Although in my case this portion of the flow was not as crucial (my data was more or less easy pickings and familiar), I did get a nice instructive window into some the issues that the "data science" crowd routinely worry about. I expect to benefit much more from this material in the future, as it is certain I'll have to confront less familiar datasets at some point. See the TOC online, as there is a lot of nice material here that, as it happens, I didn't yet need to dig into for my particular project. This time, that is.
Since I already knew most of the requirements for my visualizations, my work started in earnest with the web app. This is where Dale's book really laid out a clear, detailed path for me.
On the server side, his choice of the Flask micro web framework was a good fit, and the explanations were clear and helpful. Like many, I have in recent years become enamored of scripting in python, and Flask is a nice lightweight framework in which to easily code up a solid data delivery interface. I opted for MongoDB storage, but SQL is supported too; both are covered in the book. It's all spelled out, and it was straightforward to apply his techniques to my problem. One of the keys to making this work for me was his prescription for how to make a clean so-called REST interface, including - crucially - pagination, to throttle loads of JSON data from the server to the client via ajax.
Then, on the client side, crossfilter.js and d3.js make it possible to produce some very sophisticated (and beautiful) visualizations. Crossfilter and d3, with all their subtleties and moving parts, are not easy tools to master - at least not for me. And there is perhaps an over abundance of code snippets out there on the web, a kind of fast food diet that does not provide sufficient understanding necessary to prevent the inevitable frustration later on when this approach starts to let you down. So Dale's step by step, screenshot by screenshot walkthroughs, with excellent color diagrams, really helped tremendously here. I have not seen better explanations anywhere.
Final notes: The various sections of the book are relatively independent and can stand alone; depending on your background, you may be able to skip around and hunt for exactly what you need. If you are in a short term deadline situation, this may very well be the best approach initially. A reasonable skim of the appropriate section(s) of the book and a bit of copy-paste with the code should get you on your way.
But, speaking generally, I would tend to advise against such an approach, especially when you're not under time pressure. This book is more of a running storyline and workshop than a quick-reference cookbook. A hunt-copy-paste approach could very well blind you to the whole point, and lead you to toss the book aside, incorrectly concluding that it won't help you after all. And even if you reap some quick early victories, you may still miss out on unexpected goodies that you'll later wish you hadn't. Instead, a more careful, active study is required to gain the fluency with these tools that you actually need so that they won't crumble when you try to apply them in new circumstances. If you make the effort, this extraordinary book will show you how to gain this fluency.
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Example 3.1 with nobel_winners, where do I start?Read more