PDF Download Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
Februari 23, 2013 |
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PDF Download Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
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Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
PDF Download Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
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Book Description
Building Full-Stack Data Analytics Applications with Spark
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About the Author
Russell Jurney runs a boutique consultancy, Data Syndrome, specializing in building analytics products. He 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, LinkedIn and Hortonworks. He lives on the ocean, in the fog, in Pacifica, California with Bella the Data Dog.
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Product details
Paperback: 352 pages
Publisher: O'Reilly Media; 1 edition (June 23, 2017)
Language: English
ISBN-10: 1491960116
ISBN-13: 978-1491960110
Product Dimensions:
7 x 1 x 9.2 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#288,105 in Books (See Top 100 in Books)
As a R language programmer, I'd like a book so clear, concise, to the task and motivational as Russell's but for R . Worth every dollar
Agile and "Science" are the anthesis of each other. Science is a commitment to rationalism. Agile is a word unintelligent people use when trying to appear intelligent to other unintelligent people.So to set the record straight, I read this book cover-to-cover (unusual for me). I found it to be practical, well organized, insightful at times and overall a good introduction to the topic of Data Science.I would like to clear up something not about this book but about our entire culture-- that always wants something for nothing. You will NOT be generating deep insights about your business effortlessly or quickly. By definition, these things are difficult and time-consuming.So buy this book and get started.
This book attempts to introduce a new methodology for analytics product development. And within this scope, I feel that the book accomplishes it's stated goal. Although somewhat lengthy, the flow of information within this book stays focused on the critical path to the end product while covering documentation, facilitation, exploration, and discovery. A reappearing theme of aligning data science with the rest of the organization is present throughout.After the obligatory introductory chapters, the book introduces a suite of tools used for the remaining chapters. These include Jupyter Notebooks, Python 3, Spark, sci-kit learn, and lightweight web applications. The data it introduces is the OpenFlights Database that is freely available from the Bureau of Transportation Statistics followed by weather datasets available from NOAA. The first goal is to use the tools and the data to predict flight delays.With this setup, the book continues with detailed studies of collecting and displaying records, visualizing data, exploration of data, making predictions, deploying predictive systems, and improvements. I appreciated how the book followed the same datasets throughout as it moved through all the stages it's proposed methodology. Overall, a solid addition to the data science library.
At the outset, author Russell Jurney describes his intended audience: “Agile Data Science is intended to help beginners and budding data scientists to become productive members of data science and analytics teams. It aims to help engineers, analysts, and data scientists work wth big data in an agile way using Hadoop. It introduces an agile methodology well-suited for big dataâ€. In my opinion, he achieves all of his goals. As a computer forensics specialist, I always deal with data. What has changed in the past two decades is the scale of the data I have to analyze. We’ve come a long way from analyzing a few megabytes of data. Now, the possibility exists that I may have to deal with petabytes of data to find what I am looking for – or confirm its absence. To that extent I have to deal with people who tell me things can’t be done, usually within an adversarial relationship. What this book does for me in a big way is clarifying the process of getting from here to there. Jurney describes the process clearly and in great deal. While I am not exactly the intended audience, I think those who are will benefit greatly from this book.Jerry
I am reviewing a copy of "Agile Data Science 2.0" by Russell Jurney that I received at no cost through the Amazon Vine program.Working in a data science group in I.T., we've had a lot of conversations about how I.T. operating approaches - agile, devOps, PMO - apply to data science. Data Science tasks are different in that not all work is intended to lead to functioning software, as well as the strongly-iterative approach that is necessary to deliver results to stakeholders in a way that discrete units of software might not otherwise be reviewed.Russell Jurney's "Agile Data Science 2.0" goes a long way in moving that conversation in the right direction. I had three target audiences in mind when I acquired this book. The first was our PM, who had worked in I.T. for years as a director and project manager but continued to try to wrap his head around the data science process. The second was a director who was new to the data science process and wanted a better grasp of how to communicate expectations to the team. The third was myself, having spent time in both IT and in research, I had seen the two worlds and wanted a way to help explain how the two mesh.Jurney has offered, as have many data science books, a suggested stack and how to implement it, but the most valuable part of the book I thought was the first two chapters for their emphasis on the agile manifesto for data science, a description of the many roles that go into a team, and highlights of how agile can make for better data science both in terms of research and in terms of products.This is not a text to learn Spark from a developer's perspective but rather to understand how spark can fit in. Spark isn't the only platform, so those using Dask or other tools will still find value here.If the book has a weakness it's the focus on developing a web portal to expose the data science product; this isn't a bad way to do things, not at all, but it's not where our work is going at the moment, so this limits the applicability of some of the chapters. But there's nothing that keeps the book from being useful .. so much so that I honestly don't know whose desk it's sitting on at the moment, since as soon as our PM finished it he gave it to a BA, who gave it to another PM...
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