Personal Reading List: 1Q2018

My personal reading list for January, February, and March 2018.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.


At Bat (Schnell)


Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, by Martin Kleppmann, O'Reilly, 2017.

Fast Data Architectures for Streaming Applications: Getting Answers Now from Data Sets that Never End, by Dean Wampler PhD, O'Reilly, 2016. Copy provided by O'Reilly.

Stars-4-0._V192240704_Gradle Essentials, by Kunal Dabir and Abhinandan, Packt Publishing, 2015.

MongoDB in Action (Second Edition), by Kyle Banker, Peter Bakkum, Shaun Verch, Douglas Garrett, and Tim Hawkins, Manning Publications, 2016. 


At Bat (Langsam)


Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbott, Wiley, 2014. Copy provided by Amazon.

Common Errors in Statistics and How to Avoid Them (Fourth Edition), by Phillip I. Good and James W. Hardin, Wiley, 2012.

Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.  


On Deck


Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron, O'Reilly, 2017. Copy provided by O'Reilly.

Python Machine Learning, by Sebastian Raschka, Packt Publishing, 2015.

Python Machine Learning Cookbook, by Prateek Joshi, Packt Publishing, 2016.


In the Hole


Building Evolutionary Architectures: Support Constant Change, by Neal Ford, Rebecca Parsons, and Patrick Kua, O'Reilly, 2017. Copy provided by O'Reilly.

Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Hadoop Application Architectures: Designing Real World Big Data Applications, by Mark Grover, Ted Malaska, Jonathan Seidman, and Gwen Shapira, O'Reilly, 2015. Copy provided by O'Reilly.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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