What's poppin data nerds! It's Nick Singh & Kevin Huo (authors of your favorite data science interview prep book, Ace the Data Science Interview) here to recommend the 13 best books for Data Scientists. While many of these are about Data Science, Data Analytics, or Machine Learning, we also threw in some of our favorite business and product management books for Data Scientists. Let's face it: our field is insanely interdisciplinary, and as such, it's beneficial to read broadly.
The 3 best books to learn Data Science are Advancing Into Analytics (for people completely new to data science & analytics), R for Data Science (for a practical introduction to Data Science), and Data Science for Business (for a great introduction to high-level concepts and their applications).
If you don’t have any programming experience, but are handy at Excel, Advancing Into Analytics is the perfect gentle introduction to using R & Python for analytics. By covering fundamental concepts in Excel first, and then showing how they directly translate into a programming language, this book eases you into data analytics making it the best book for total beginners.
R for Data Science is the perfect hands-on introduction to Data Science. The book does a great job balancing implementation details in R while also giving you a big-picture understanding of the data science process. One caveat: if you do have previous experience with programming, especially in Python, it’s best to skip R and just dive into the Python data analysis stack instead.
Data Science for Business is a great conceptual introduction to Data Analytics and Data Science. The authors do a great job showing the business applications of various techniques, as well as the meta-concerns Data Scientists need to be concerned with. However, it lacks practical exercises and code snippets, making it not a great hands-on book. As such, we recommend this book to people who need to be familiar with Data Science at a high-level, but don’t need to be responsible for implementing data science details in their day-to-day work.
The 3 best books to learn Machine Learning are Intro to Statistical Learning for the hard-core theory behind ML, the Hundred-Page Machine Learning book for a quicker crash-course into the math and concepts behind ML, and Hands-On Machine Learning with Scikit-Learn and TensorFlow for a practical tutorial.
Intro to Statistical Learning (& it's even harder cousin, Elements of Statistical Learning) are both free & amazing resources for learning machine learning theory. For Data Science & Machine Learning practitioners, it's never a waste of time to brush up on your fundamentals! While hailed as the bible of ML, be warned: it's challenging to read and most people give up after a few chapters!
For a lighter introduction to the fundamentals of machine learning, this 100 page book (well...137 pages but who's counting) strikes the right balance between enough math to explain the central ideas in ML, without overwhelming the reader.
True to its name, this book is the best hands-on introduction to Machine Learning. Hands-On Machine Learning is rich in concrete examples, and light on theory, making it the perfect read for someone who is already familiar with the fundamentals of Data Science and ML but is now hungry to tangibly apply what they know.
The 3 best books for Data Scientists who are trying to succeed in their career and land data science jobs are Ace the Data Science Interview for interview prep, the Data Science Handbook for career and life insights from top Data Scientists, and So Good They Can't Ignore You to help you more broadly design a successful career.
Ace the Data Science Interview is the best book to prepare for a Data Science Interview. It covers the most frequently-tested topics in data interviews like Probability, Statistics, Machine Learning, SQL, Coding (Python), and Product Analytics. With 201 data science interview questions to practice with, this book is a must-read for those trying to land data jobs at FAANG, tech startups, or on Wall Street. It’s also a great book to prepare for Data Analyst and Machine Learning interviews too.
Of course, we wrote this Amazon Best-Seller, so we’re a tiny bit prejudiced!
This light-read interviewed 25 leaders in Data Science - both Data Science thought leaders like DJ Patil, as well as Data Science practitioners who are leading the most innovative data teams at companies like Airbnb, Netflix, and Facebook. It has a mix of career advice for Data Scientists, perspectives on the field, and general life advice.
In this book, Cal Newport debunks the career advice of “follow your passion". Instead, he provides the evidence-based framework for finding work you’ll love. Newport’s big idea is that becoming excellent at a skill the world finds valuable is an ideal path towards career satisfaction and success. We recommend this book to anyone confused or frustrated about their current situation.
The 4 books we recommend Data Scientists to read to improve their business intuition and product-sense are the Personal MBA, BCG's on Strategy, Lean Analytics, and the Product Management classic Inspired.
Let’s face it: as a Data Scientist, often your project’s success isn’t based on the cleverness of your technical solution, but on your ability to work effectively with business stakeholders. So, how do you work better with business people? Speak their language! This book is essentially a crash-course on the most important terms, concepts, and mental models in business, at 0.01% of the price of going to business school.
Want to be a better “big-picture” thinker (whatever that means!). This book, written by many partners at BCG, talks about concepts like organization design, change management, and developing business strategies. The frameworks and terminology in this book have permeated boardrooms everywhere - it’s much bigger than BCG! If you're frequently presenting data-driven recommendations to the C-Suite, or doing analysis that informs the company’s larger strategic vision, you need to read this book.
Lean Analytics is valuable to anyone working in product data science, product analytics, or marketing analytics. The book walks through the most important metrics to measure for a variety of tech business models. Curious about what analytics Instagram should measure? Just read the chapter on User-Generated Content websites. Have a data analytics interview at Uber coming up? Just read the chapter on Two-Sided Marketplaces. Very practicable, actionable insights that will help you measure what matters.
Inspired beautifully combines Marty Kagan's own personal lessons in Product Management, with insights from the product culture at Amazon, Netflix, and Tesla. Our favorite sections are on Product Roadmaps and Product Vision — reading these parts of the book helped us work and communicate more efficiently with the PMs on our teams at Facebook. If you want to be bossed around by PMs and managers less, and instead take a more proactive role in what products are built, read this book.
Nick Singh is a former Software Engineer at Facebook & Google, now turned career coach. His career advice posts on LinkedIn have garnered over 10 million views. Kevin Huo is a former Data Scientist at Facebook, and now a quant on Wall Street. He's helped coach hundreds of people to land data jobs at Amazon, Two Sigma, and Lyft. Together they wrote the Amazon #1 Best-Seller, Ace the Data Science Interview, which solves 201 real Data Science & Machine Learning interview questions from FAANG, Tech Startups, and Wall Street.