This book attempts to introduce a new methodology for analytics product development the book accomplishes it’s stated goal. Although somewhat lengthy, the flow of information within this book stays focused on the critical path to the end product while covering documentation, facilitation, exploration, and discovery. A reappearing theme of aligning data science with the rest of the organization is present throughout.
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. Youíll learn an iterative approach that lets you quickly change the kind of analysis youíre doing, depending on what the data is telling you. Publish data science work as a web application and affect meaningful change in your organization.