Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server

by Joshua Cook

1. Introduction
2. Docker3. Jupyter4. Docker Client5. The Dockerfile6. Docker Hub7. The Opinionated Jupyter Stacks8. The Data Stores9. Docker Compose10. Interactive Development

Genres: Programming

284 pages, Paperback

Published August 28, 2017

Reviews

[Guilherme · Rating 4 out of 5]

The book focuses on teaching a tool - Docker and a procedure to perform data science in a modular way. It does not necessarily focus on other tools used throughout the book, what I thought was a great compromise. It really resonated with me since I had a similar work flow, but in a non dockerized approach. Sometimes the examples are verbose, and I appreciated, sometimes not so. To me if a application/data science project were fully carried out with the framework proposed the book would be formidable but I do understand it would drive away from the book proposition in first place.

[Matt Heavner · Rating 4 out of 5]

Despite a few bad typos/grammar-os (mostly in the text), this is a great, broad look at using Docker w/ Jupyter notebooks including networking, cloud, multiple databases or data stores. Really good. I read through it all and will definitely return to work through multiple parts of it again.

[Om Lalchandani · Rating 5 out of 5]

I think this is a must read book for any data scientist. It is a prized possession for a data analyst and professional.