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Agile Data Science: Building Data Analytics Applications with Hadoopby Russell Jurney
Synopses & Reviews
Mining big data requires a deep investment in people and time. How can you be sure youre building the right models? With this hands-on book, youll 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. Youll learn an iterative approach that enables you to quickly change the kind of analysis youre doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.
Mining data requires a deep investment in people and time. How can you be sure youre building the right models? What tools help you connect with the customers needs? With this hands-on book, youll learn a flexible toolset and methodology for building effective analytics applications.
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.
Table of Contents
PrefaceSetupChapter 1: TheoryChapter 2: DataChapter 3: Agile ToolsChapter 4: To the Cloud!Climbing the PyramidChapter 5: Collecting and Displaying RecordsChapter 6: Visualizing Data with ChartsChapter 7: Exploring Data with ReportsChapter 8: Making PredictionsChapter 9: Driving ActionsColophon
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