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.
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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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Seriously Good Software
Author: Marco Faella Publisher: Manning Date: March 2020 Pages: 328 ISBN: 978-1617296291 Print: 1617296295 Kindle: B09782DKN8 Audience: Relatively experienced Java programmers Rating: 4.5 Reviewer: Mike James Don't we all want to write seriously good software?
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Artificial Intelligence, Machine Learning, and Deep Learning (Mercury Learning)
Author: Oswald Campesato Publisher: Mercury Learning Date: February 2020 Pages: 300 ISBN: 978-1683924678 Print: 1683924673 Kindle: B084P1K9YP Audience: Developers interested in machine learning Rating: 4 Reviewer: Mike James
Another AI/ML book - is there room for another one?
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