Klaviyo Data Science Podcast EP 15 | Books every data scientist should read (vol. 2)

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

(More) 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 — in fact, we spent an entire episode talking about three ways to level up your data science knowledge and skills. This month, we’re back with three more:

• One of the foremost foundational texts for understanding machine learning models in a statistical way
• A survey course for a broad variety of machine learning models, with the opportunity to go in depth on topics like deep learning
• A foundational text in designing and analyzing experiments — both in ideal scenarios and in cases where the standard assumptions aren’t met

Mentioned this episode

We discuss the following books and courses in this episode:

• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: https://web.stanford.edu/~hastie/ElemStatLearn/
• Kirill Eremenko’s A-Z courses on data science, machine learning, artificial intelligence, and deep learning
• Field Experiments: Design, Analysis, and Interpretation by Alan Gerber and Donald Green: https://wwnorton.com/books/9780393979954

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
• Nuvan Rathnayaka, Statistician at NoviSci
• Chad Furman, Senior Software Engineer
• David Lustig, Data Scientist

Edited by: Michael Lawson

Logo by: Griffin Drigotas, Ally Hangartner from Klaviyo Design