Agile Data Science
Building Data Analytics Applications with Hadoop
Publisher: O'Reilly Media
Released: October 2013
Pages: 178

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
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oreillyAgile Data Science
 
5.0

(based on 3 reviews)

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Pros

  • Concise (3)
  • Helpful examples (3)

Cons

    Best Uses

    • Intermediate (3)
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    (1 of 1 customers found this review helpful)

     
    5.0

    Just great

    By John G

    from Stamford, CT

    About Me Designer, Developer

    Verified Buyer

    Pros

    • Accurate
    • Concise
    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

      Best Uses

      • Intermediate
      • Novice

      Comments about oreilly Agile Data Science:

      Very satisfied with this book. I covers material that matters in a way you can learn it and actually use it.

      (4 of 4 customers found this review helpful)

       
      5.0

      A must read for anyone starting BigData

      By ArthurZ

      from Canada

      About Me Developer

      Verified Reviewer

      Pros

      • Concise
      • Easy to understand
      • Helpful examples
      • Well-written

      Cons

        Best Uses

        • Intermediate

        Comments about oreilly Agile Data Science:

        There are at least two reasons to read this book:

        1) The author understands that a typical business today cannot wait for a Data Scientist for too long to deliver results demanding as usual a very quick turnaround on investments (ROI), you will be able to cope with the demand and
        2) The book covers all the needed and proven modern brick and mortar offerings to get the job done by a relatively newcomer to the Big Data World.

        It certainly enables such a professional to grow and expand based on the acquired knowledge, and one can truly do it very fast.

        (11 of 11 customers found this review helpful)

         
        5.0

        Great practical guide to tools and tech

        By aaron

        from Philadelphia, PA

        About Me Developer, Manager

        Verified Reviewer

        Pros

        • Concise
        • Helpful examples

        Cons

          Best Uses

          • Intermediate

          Comments about oreilly Agile Data Science:

          I'm really enjoying going through this big data tutorial and learning much.

          Interestingly I've toyed with nearly all the technologies being used and thought I understood the value of big data. I even have some map-reduce analytic jobs running to provide real value.

          This book made the 'agile' part click and made me look at my analytic workflow like any other software process. Just like I focus on optimizing my tooling for automation/compiling/testing applications I see how easy it could be to have a similar workflow to BI.

          I like the writing style and the pace. He calls out some common traps while not spending too much time going into installation and tool details best left to the project websites.

          I'd like to see a part II of this where these techniques are blended with SQL data and maybe data warehouses.

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