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Agile Data Science: Building Data Analytics Applications with Hadoop, by Russell Jurney
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Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.
Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.
- Create analytics applications by using the agile big data development methodology
- Build value from your data in a series of agile sprints, using the data-value stack
- Gain insight by using several data structures to extract multiple features from a single dataset
- Visualize data with charts, and expose different aspects through interactive reports
- Use historical data to predict the future, and translate predictions into action
- Get feedback from users after each sprint to keep your project on track
- Sales Rank: #924244 in Books
- Brand: Brand: O'Reilly Media
- Published on: 2013-10-28
- Released on: 2013-10-28
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .38" w x 7.00" l, .66 pounds
- Binding: Paperback
- 178 pages
- Used Book in Good Condition
About the Author
Russell Jurney cut his data teeth in casino gaming, building web apps to analyze the performance of slot machines in the US and Mexico. After dabbling in entrepreneurship, interactive media and journalism, he moved to silicon valley to build analytics applications at scale at Ning and LinkedIn. He lives on the ocean in Pacifica, California with his wife Kate and two fuzzy dogs.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Frustrating but beneficial.
By Kev
This book is terribly frustrating. I have a lot of experience in statistical analysis, machine learning, and programming and picked up this book to learn how to apply these skills with "Agile Data Science." This mostly means implementing these skills with the tools of AWS, Hadoop, and hosting data products on scalable platforms.
The book does a great job of summarizing the agility needed and the tools used, but the code to implement these tools is lacking. I expected the code in the book to be outdated even though the book was only published a year ago. There is a github repository for the code but it is incomplete.
One benefit of the code not working or the instructions being vague is having to debug it yourself or search for solutions. This is a great learning tool. I don't think this is a benefit the author would like and should put some more time in the second iteration of making the instructions clearer. Since the book has only 164 pages there is significant room for growth.
15 of 17 people found the following review helpful.
He sent this with NON-WORKING CODE
By SF
The story is nice, but the code that forms the basis of the entire project behind the book DOESN'T COMPILE. The author has - as of today (June 9, 2014) - removed all of the github references to the project.
I"m half way through the book, have been practicing Agile development techniques for several years, and I am not quite sure what in particular makes this book about Data Science 'Agile' based.
One thing that he does nicely is explain the Pig code he uses, but I can't use those programs because the Python programs that gather the data that feed Pig will not compile, even after I de-bugged his code for several hours. (Example: the author made reference to an RFC inline in the Python code that would have NEVER compiled. NEVER. Line 11 gmail.py from call to email utilitiies)
16 of 18 people found the following review helpful.
Absolute required reading for all new data scientists
By chad
I was once told by a chief data scientist that they would rather teach a mathematician programming than a programmer math (to be a data scientist). After being a data scientist for some time now I would have to respectfully disagree. 85% of data science is plumbing and I wouldn't hire a physicist to be a plumber. Indeed modern data scientists really do need to be full-stack developers trapped in an academic's body.
Jurney nails it! He offers tools and methodologies adapted to common data science workflows and their associated pitfalls wherein we spend 85% of our time plumbing and 15% of our time integrating some off-the-shelf algorithm to find deep insight.
So, for new data scientists or 3rd-4th year grad students who have balanced their Twitter API hack with NSF grant deadlines, this is ABSOLUTELY REQUIRED READING.
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