As recommended by Professor Koo, <beautiful data> was chosen as the sample of well written document for our final reports.

O'Reilly Media, the author of this book said that, “In this insightful book, you'll learn from the best data practitioners in the field just how wide ranging and beautiful working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.”

It’s like a thought-provoking tour of recent data-intensive research for me. I found it most useful for its "aha" and "I should have thought of that" moments. I also appreciated the numerous external references to free datasets and data processing tools.

Inspired by the points of view in this book, I Rethink the meaning of database skills used in our project, and write them in the final report the same as the book. The following two topics are interesting and meaningful for our project.

First, Data visualization is often all about analytics and technical results, but it does not have to be especially with personal data collection. People who collect data about themselves are not necessarily after the actual data. They are mostly interested in the resulting information and how they can use their own data to improve themselves. For that to come through, people have to see more than just data in the visualization. They have to see themselves. Life is complex, data represents life, and users want to understand that complexity somehow. That does not mean we should dumb down the data or the information. Instead, we use the data visualization to teach and to draw interest. Once there is that interest, we can provide users with a way to dig deeper and explore their data, or more accurately, explore and understand their lives in that data. It is up to the statistician, computer scientist, and designer to tell the stories properly. As our project well realized the data visualization, I think we should dig out its true meaning in our final report.

Second, I think the audience that stands to benefit this most from this book are low level managers and people in charge of large amounts of data that they don't know what to do with. The reason for this is that while there are a few chapters that deal with low level implementation details it mostly consists of overviews of popular and successful mentalities surrounding data. One other type of audience that might be a target for this book would be young college students with interests in math, statistics or computer science. All these factors appear the same in our project, as the function of our plug-in is to track the learning process, dealing with large amount of data and finally visualize them.

Every chapter in <beautiful data> offered me something that I won't forget. More importantly, most chapters offered a data source or data processing tool that expanded my toolbox of things to use when programming. The only reason this book misses a perfect 10/10 from me is chapter six and a couple of the later chapters feeling like weaker ideas from earlier chapters rehashed into a different domain. A worthwhile book if you work with data — whether you be a consumer or producer.

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