Best Data Science Books You Should Read!
A beginners journey to data science from zero to hero!
There’s a lot of data science materials to read and study and keeping track of them is getting harder by the day. In this vast ocean of books, videos, blogs, or to sum it up the data, finding what you have to read can be a cumbersome task!
This post assumes you already have beginner level skills in python. Although there’s no specific order to the reading of these materials, I’d still recommend you to go with them in the order I have outlined.
You’re on the beginning of your journey. You have just found out the high salary for data scientist and the money has made you blind. you know data scientist is the sexiest job of the 21st century and you want to become one. Time to start with Exploratory Data Analysis (EDA). Learn how to connect the dots with a simple yet useful guide.
- Think Stats: Exploratory Data Analysis — by Allen B. Downey
This book will help you understand probability and statistics through writing code, simulations, importing data,cleaning data, and using statistical inference to answer real world questions.
Now you might be having second thoughts. Do I have to really have a sexy job title? Why Python? I thought pandas are lazy! What are all these plots, statistics, functions, data frames, numbers?! Do not fear baby Jedi! Use the force of Bayes theorems in statistical inference!
2. Bayesian Methods for Hackers — by Cameron Davidson-Pilon
The book improves your understanding for the motivations, applications, and challenges in Bayesian statistics and probabilistic programming from a computation and a mathematical point of view.
Congrats! Now you have mastered the Bayesian methods! You’re friends call you a math geek and come to you for their probability questions! You feel like you’re up for the challenge!
3. Understanding Machine Learning — by Shai Shalev-Shwartz and Shai Ben-David
Read this book to get a clear cut understanding of Machine Learning and all of its paradigms from basics to the mathematical algortihms.
Although you might feel like you have an idea about ML, you’re not done yet! Your first mission is to go mining! You will learn how to mine data by doing it!
4. A Programmer’s Guide to Data Mining — by Ron Zacharski
This is a fantastic guide that puts you through exercises to master the foundations of data mining.
“Before you is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides.” — quote from the author
All those practices and the mining hardships makes you to want to analyze them. Time for some applied statistics and more machine learning!
5. An Introduction to Statistical Learning — by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
ISLR is one of the best books available with extra online materials that teaches minor statistics and machine learning models in the R programming language that you can apply in real life settings. If you are up for the challenge, feel free to do the labs in the python way!
All those labs and practices makes you lose hair and you’ll get a big mustache! You’re still determined. You have started to love Tibshirani and Hastie’s books and you want more!
6. The Elements of Statistical Learning — by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
A valuable resource for anyone interested in data mining or the industry.
You feel like you know everything, yet you still lack the foundations of the data science! Get ready to go into the future!
7. Foundations of Data Science — by Avrim Blum, John Hopcroft, and Ravindran Kannan
A book with a look to the future, aiming to gather all the theory of data science that’ll be useful in the next 40 years.
Now you know the data science relativity theory. You’re a creative thinker and all your Es are equal to mc² !
Time to get some Stanford knowledge!
8. Mining of Massive Datasets — by Jure Leskovic, Anand Rajaraman and Jeff Ulman
A concise book and a great read!
Now you feel confident enough to learn deep learning and I mean the whole thing!
9. Deep Learning an MIT Press Book — by Ian Good fellow, Yoshua Bengio and Aaron Courville
A complete reference on deep learning unlike any other. Just search “deep learning” in google scholar and you’ll see this book has been already cited by more than five thousands of research papers.
Cool! You have never felt this much confidence in your abilities.
10. Machine Learning Yearning — by Andrew NG
A graduate level machine learning book that aims to quickly allow you to gain practical skill without going into a graduate program.
Most of the posted material is available for free yet if you wanted the kindle version or the hardcover, feel free to get them from Amazon. I will update this post with more worthy mentions!
So what are you waiting for? Grab coffee and read them to get into the data science world!
