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
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About Klaviyo
Klaviyo helps growth-focused ecommerce brands drive more sales with super-targeted, highly relevant email, Facebook, and Instagram marketing. Interested? We’re always looking for great people to join our team.
Who’s who
- Michael Lawson, Senior Data Scientist
- Charlie Natoli, Data Scientist
- Woody Austin, Machine Learning Engineer
- Chris Fox, Senior Data Scientist
- Paul Langton (github), Machine Learning Engineer
Edited by: Aaron Goeglein
Logo by: Griffin Drigotas, Ally Hangartner from Klaviyo Design
