Agile Data Science 2.0

Russell Jurney

ISBN 10: 1491960116 / ISBN 13: 9781491960110

Published by O'Reilly Media, US, 2017

About this Item

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they're to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You'll learn an iterative approach that lets you quickly change the kind of analysis you're doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization.Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track.

Seller Inventory # LU-9781491960110

Bibliographic Details

• Title: Agile Data Science 2.0
• Publisher: O'Reilly Media, US
• Publication Date: 2017
• Language: English
• ISBN 10: 1491960116
• ISBN 13: 9781491960110
• Binding: Paperback
• Condition: New
• Weight: 666 grams

About this title

Synopsis

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.

Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization.

• Build value from your data in a series of agile sprints, using the data-value pyramid
• Extract features for statistical models from a single dataset
• Visualize data with charts, and expose different aspects through interactive reports
• Use historical data to predict the future via classification and regression
• Translate predictions into actions
• Get feedback from users after each sprint to keep your project on track

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.