Klaviyo Data Science Podcast Ep 11 | Books every data scientist should read (vol. 1)

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into…

Required reading for data science

A question we frequently get asked is: what books should I read to be a better data scientist/machine learning engineer? This may not surprise you, but there isn’t just one answer — depending on the skills you have, your knowledge base, the point of your career that you’re in, and many other factors, there are many books you could read that will help you learn more. This month, we cover several ways to improve the skills you need to contribute to a data science team. You’ll hear about all that and more, including:

• Object-oriented programming, how to think about it practically, and how it can help anyone on a data science team
• The ethics of machine learning and AI, and why understanding AI ethics is one of your most powerful tools
• How Pac-Man delivers some of the most powerful data science insights of our time

Mentioned this episode

Some more reading or viewing that we mention in this episode:

• Practical Object-Oriented Design in Ruby by Sandi Metz: https://www.poodr.com/
• Sandi Metz’s keynote: https://www.youtube.com/watch?v=8bZh5LMaSmE
• Weapons of Math Destruction by Cathy O’Neil: https://weaponsofmathdestructionbook.com/
• Northeastern CS 4100: https://www.ccs.neu.edu/home/jwvdm/teaching/cs4100/fall2019/
• UC Berkeley CS 188: https://inst.eecs.berkeley.edu/~cs188/pacman/home.html

Contact us

The best place to reach the podcast is by messaging me on Twitter: https://twitter.com/lawson_m_t.