Agile Data Science 2.0

Author: Russell Jurney
Publisher: O'Reilly
Pages: 352
ISBN: 978-1491960110
Print: 1491960116
Kindle: B072MKL34K
Audience: Data scientists
Rating: 4.5
Reviewer: Kay Ewbank

This practical book works through the tools and techniques for modern data analysis of structured and unstructured data, showing how to create data analysis applications using Spark and Kafka.

The book opens with a chapter on the theory of agile data science as being centered around web application development. The author sets out a 'manifesto' for agile data science as being organized around iterative development, shipping intermediate output, prototyping experiments, integrating what the data forces on you, taking the data-value pyramid into consideration, and identifying the critical path. The next chapter looks at what agile tools are available and what their benefits are, introducing Spark, MongoDB, Kafka, PySpark Streaming, Spark MLib, and Apache Airflow. 

 

Banner

The Data side is next to be introduced, both the sample data that will be used for the rest of the book and a discussion of SQL versus NoSQL.

Part two of the book is titled Climbing the Pyramid, by which Jurney means the Data-value pyramid. At the bottom of the pyramid come records, the collection, displaying of individual records. The next level up is charts, or more specifically the process of cleaning, aggregating, visualizing and questioning the data. Reports are next highest, then predictions, and at the top level are actions based on what you've learned getting so far.

Having explained the pyramid, Jurney starts at the bottom with how to collect and display records using a combination of MongoDB, Flask, pymongo, Jinja2, and Elasticsearch.

Visualizing data with charts and tables is next on the agenda, along with 'data enrichment' - bringing data in from another dataset to add more information. This is followed by a chapter on exploring data with reports using PySpark, Mongo, Flask, and SparkSQL.

The most useful portion of the book comes next, with three good chapters on predictions, starting with a chapter on making predictions that covers predictive analytics using PySpark and scikit-learn, along with building a classifier with Spark MLib.

Next is a good chapter on deploying predictive systems using scikit-learn, Spark ML, MongoDB, and sending prediction requests to Kafka. The final chapter is on improving predictions and how to tune the Spark ML classifier.

Overall, I liked this book. and although I did have a feeling at times that the author was re-inventing the wheel of the standard database application, the tying together of the different components was well explained and carried out. The material is well organized, and there are plenty of good code samples and a useful appendix. The best thing is that the author stayed very practical throughout the book. If you want to learn how to use the tools described, this is a good introduction.

To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin.

Banner


Computer Graphics Programming in OpenGL with Java (2e)

Author:  V. Scott Gordon
Publisher: Mercury
Date: September 2018
Pages: 406
ISBN: 978-1683922193
Print: 1683922190
Kindle:B07HNF4YSN
Audience: Java developers interested in 3D graphics
Rating: 5
Reviewer: Mike James
Java and graphics aren't a natural pairing - are they?



MongoDB The Definitive Guide, 3rd Ed

Author: Shannon Bradshaw, Eoin Brazil and Kristina Chodorow
Publisher: O'Reilly
Pages: 514
ISBN: 978-1491954461
Print: 1491954469
Kindle: B082J7DMBX
Audience: MongoDB developers and administrators
Rating: 5
Reviewer: Kay Ewbank

This popular book, regarded by many as the MongoDB bible, has been update [ ... ]


More Reviews

Related Reviews

Agile Data Science

Big Data Analytics

Data Science and Big Data Analytics

Doing Data Science

Fast Data Processing with Spark (2nd Ed) 

For more book recommendations see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

Last Updated ( Saturday, 25 November 2017 )