Personal Reading List: 2Q2022
My personal reading list for April, May, and June 2022.
Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.
At Bat (Schnell)
How Will You Measure Your Life?, by Clayton M. Christensen, James Allworth, and Karen Dillon, Harper Business, 2012.
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Second Edition), by Christoph Molnar, Independently Published, 2022.
Machine Learning Engineering in Action, by Ben Wilson, Manning, 2022. Copy provided by author Ben Wilson.
At Bat (Langsam)
Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.
The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.
Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.
On Deck
Enterprise AIOps: A Framework for Enabling Artificial Intelligence, by Justin Neroda, Steve Escaravage, and Aaron Peters, O'Reilly, 2021. Copy provided by Booz Allen Hamilton.
Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, by Jen Fisher and Anh Phillips, McGraw Hill, 2021. Copy provided by Deloitte.
Running with Purpose: How Brooks Outpaced Goliath Competitors to Lead the Pack, by Jim Weber, HarperCollins Leadership, 2022. Copy provided by Amazon.
In the Hole
AWS Certified Database – Specialty (DBS-C01) Certification, by Kate Gawron, Packt Publishing, 2022. Copy provided by Packt publicist Nivedita Singh.
Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.
Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.