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

Klaviyo Data Science Podcast

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