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Summer Tech Must-Reads for Data Science

By Peter BellJuly 19, 2019
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Just because it’s summertime, that’s no reason to stop learning. Here are five great must-read books to bring to the beach depending on where you are in your journey towards becoming a Data Scientist.

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Data Science for the Layman by Annalyn Ng and Kenneth Soo

If you’re just starting to explore Data Science and don’t know a decision tree from a support vector machine, this is the book for you! In 125 well-thought-out pages, the book explains how the data science process works and introduces some of the most popular algorithms used by data scientists.

While you won’t be able to train a neural network or perform a logistic regression after reading “Data Science for the Layman,” you’ll understand the key terminology used by Data Scientists and will have a good sense for what they do. This is a great book to start your data science journey whether you want to become a data scientist or just want to understand them.

Naked Statistics by Charles Weelan

If you aspire to become a professional Data Scientist, you might want to spend the summer brushing up on your statistics. Sure, that might sound like as much fun as a root canal, but Charles Weelan really does manage to “strip the dread from data” — just like the subtitle promises.

If you’ve ever wondered how central limit theorem can help you figure out whether a broken down bus is going to a marathon or the International Festival of Sausage, get a copy of this book and learn just how much fun statistics can really be!

Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron

This weighty (525 page) book is both impressive and daunting in equal measure. If you have a strong background in programming, are comfortable going fast, and want a hands on introduction to both classic Machine Learning and Deep Learning, it’s a thoughtful and practical introduction to the field.

Written by Aurélien Géron, who led the YouTube video classification team from 2013 to 2016, it’s clearly written by a practitioner and focused on the concepts you need to succeed as a Data Scientist. Just make sure not to get sand in your keyboard! 

Reinforcement Learning - An Introduction by Richard S. Sutton and Andrew G. Barto

This is by no means an easy or introductory text, but if you’re familiar with neural networks and aren’t fazed by math, it’s a really interesting overview of the fast moving field of reinforcement learning.

How to Measure Anything by Douglas W. Hubbard

One of the biggest challenges as a Data Scientist often isn’t in analyzing the data, but in obtaining it. This book provides a range of practical techniques for assigning value to the intangibles that are often most important to a business.

Whether you’re a manager looking to better measure your team’s output or a Data Scientist looking to broaden your toolkit, it provides a powerful framework for reasoning about the parts of a business that you might never have been able to measure before.

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Peter Bell

Head of Data Science

Peter is a veteran technologist, CTO, entrepreneur, and longtime educator, having taught digital literacy at Columbia and authored numerous programming books.

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