With a subtitle of Building Full-Stack Data Analytics Applications with Spark, the revised second edition of this hands-on guide, shows 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.
Author: Russell Jurney Publisher: O'Reilly Date: June 2017 Pages: 352 ISBN: 978-1491960110 Print: 1491960116 Kindle: B072MKL34K Audience: Data scientists Level: Intermediate
- 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
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Artificial Intelligence For Developers (In Easy Steps)
Author: Richard Urwin Publisher: In Easy Steps Date: September 2024 Pages: 192 ISBN: 978-1787910119 Print: 1787910113 Kindle: B0DBHZRZGM Audience: Developers interested in AI Rating: 4 Reviewer: Mike James So many books on AI why another?
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Facilitating Professional Scrum Teams (Pearson)
Author: Patricia Kong, Glaudia Califano and David Spinks Publisher: Pearson Pages: 320 ISBN: 978-0138196141 Print: 0138196141 Kindle: B0CLKZC5JM Audience: Scrum managers Rating: 5 Reviewer: Kay Ewbank
This book sets out to "Improvement, Effectiveness and Outcomes". How does it fa [ ... ]
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