Free PDF Principles of Data Wrangling: Practical Techniques for Data Preparation
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Principles of Data Wrangling: Practical Techniques for Data Preparation
Free PDF Principles of Data Wrangling: Practical Techniques for Data Preparation
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About the Author
Tye Rattenbury is Trifacta's lead data scientist. He holds a Ph.D. in Computer Science from UC Berkeley. Prior to Trifacta, he was a Data Scientist at Facebook and the Director of Data Science Strategy at R/GA.Joe Hellerstein is Trifacta’s Chief Strategy Officer and a Professor of Computer Science at Berkeley. His career in research and industry has focused on data-centric systems and the way they drive computing. In 2010, Fortune Magazine included him in their list of 50 smartest people in technology, and MIT Technology Review magazine included his Bloom language for cloud computing on their TR10 list of the 10 technologies "most likely to change our world".Jeffrey Heer is Trifacta’s Chief Experience Officer and a Professor of Computer Science at the University of Washington, where he directs the Interactive Data Lab. Jeffrey’s passion is the design of novel user interfaces for exploring, managing and communicating data. The data visualization tools developed by his lab (D3.js, Protovis, Prefuse) are used by thousands of data enthusiasts around the world. In 2009, Jeffrey was named in MIT Technology Review’s list of "Top Innovators under 35".Sean Kandel is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.Connor Carreras is Trifacta’s Manager for Customer Success, Americas, where she helps customers use cutting-edge data wrangling techniques in support of their big data initiatives. Connor brings her prior experience in the data integration space to help customers understand how to adopt self-service data preparation as part of an analytics process. She holds a B.A. from Princeton University.
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Product details
Paperback: 94 pages
Publisher: O'Reilly Media; 1 edition (July 15, 2017)
Language: English
ISBN-10: 1491938927
ISBN-13: 978-1491938928
Product Dimensions:
7 x 0.2 x 9.2 inches
Shipping Weight: 4 ounces (View shipping rates and policies)
Average Customer Review:
2.5 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#511,258 in Books (See Top 100 in Books)
I'm a self-acknowledged O'Reilly fan -- I normally think they do a great job of publishing great stuff for nerds like me. But I'm really, really glad I didn't pay for this book. It's 82 pages of high-level discussion of things that might be relevant to your data project but probably aren't.The subtitle is "Practical Techniques for Data Preparation" but that's a bald-faced lie. There's very little practical content in this book -- most of it is superficial and quite far from the day-to-day concerns that a data professional encounters. Most of what's in the book are things that are probably solved by default for you. "What kind of structure should I keep my data in?" Well, if you're using SQL, Excel, or any of the other bog-standard tools you're going to find on your work-issued computer, then surprise, the answer is tabular.Moreover, there's not nearly enough content to justify publishing this pamphlet. Things that I do a lot -- like combining multiple data sets -- got literally one page worth of discussion. Other things that I do a lot -- like wrestling with JSON vs CSV vs XLSX, etc -- aren't really touched on at all. This isn't even a mile wide and an inch deep; it's a foot wide and a millimeter deep.And the content that is there isn't always relevant. For unknown reasons, towards the end of the book, the authors devote 5 pages to explaining the differences between a data scientist and a data analyst, and what their roles are in the data ecosystem. Why? Couldn't tell you, because the distinction certainly isn't a "practical technique for data preparation."I do not recommend this book.
Brevity is the soul of wit, and doubly so the soul of good technical instruction. This is where this book flounders. This book feels like a powerpoint presentation gone inexplicably wrong. Its generic project management advice, with a thin veneer of "Data Wrangling" mixed in. It's a list of steps one should take in a data wrangling project without any real insight into what it takes to do them well. If you truly know nothing about Data Wrangling and have lacked all ambition and common sense up until this point, this may be the book for you.
"Principles of Data Wrangling: Practical Techniques for Data Preparation" by Tye Rattenbury, Joseph M. Hellerstein, Jeffrey Heer, Sean Kandel, and Connor Carreras comes in at a very lean 82 pages. While it does have some interesting points, there isn't a lot of new information contained within. It is definitely meant for professionals who specialize in the field which also means that the expectations are higher. I think it is worth a look but perhaps at a discounted price.
It's not clear what audience the authors were aiming towards - principles of data wrangling sound like something that a data scientist or an analyst would do...but every single one of them will know the information in this book (and more than that)! It's not a good "primer" for a manager, as it's too in the weeds and so much fluff that it wouldn't give a manager enough information. Can't recommend.
I gave this book 4 stars as it serves the function of brining together a number of, perhaps not-so-earth shattering, ideas into a framework, and concise summary. The brevity of the work can be seen as advantageous.I think the word "techniques" in the subtitle is extremely misleading - if you set your expectations of the book focused on the word "Principles" you won't feel mislead.As someone who has worked for the last 20 years in 'Data and Analytics' this book is a great read for the influx of software developers breaking into 'Data' given the current sexiness of "Big Data Science".I think the word "techniques" in the subtitle is extremely misleading - if you set your expectations of the book focused on the word "Principles" you won't feel mislead.
The book is very vague and simplistic. I agree with the other reviewers. There is a lot of fluff, and it's very short. I don't know for whom exactly this book was meant. I would not recommend.
I'm confused why this was published as a book. And the price of this book for 94 pages of not a lot of information that, quite frankly, seems kind of obvious. I think if you're trying to process large amounts of data you could do some Internet searches and get much of what you need.
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