Data science is a growing and constantly evolving field that is becoming increasingly competitive. If you’re interested in data science and want to stay ahead of the curve, one of the best things you can do is stay up to date and refine your data skills.
While the field of data science can be challenging, it is full of opportunities. Getting your hands on great data science books will allow you to strengthen your problem-solving skills and will prepare you to solve real-world data issues. We’ve put together a list of books that will help you understand the world of data science, no matter what your current level of competence and expertise is.
By Andrew Bruce, Peter C. Bruce, and Peter Gedeck
If you’re thinking about beginning a career in data science, the crucial concepts in this book will give you a great overview of the field. This book is for anyone looking to learn the basics when it comes to data science concepts, including sampling distributions, randomization, and resampling. The practical examples provided with each concept make the book easy to understand and ideal for beginners.
The authors address the significance of statistics in data science throughout the text. Advanced level data scientists are unlikely to learn anything new from this one, but beginners will gain a strong grasp of the essentials and take away plenty of practical knowledge.
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
By Seth Stephens-Davidowitz
It’s not every day you find a data science book with a humorous touch. Everybody Lies is one of those rare data science texts that features engaging, amusing examples. You’ll find insights into how big data plays a role in our everyday lives, finding its way into areas like the economy, gender, race, and sports. The book highlights that big data has always been a part of our lives – we just may not have always noticed. According to Seth Stephens-Davidowitz, big data is “digital truth serum”. It dishes up the truth that people don’t always share.
Overall, the book forces the reader to think about the importance of big data and privacy. The case studies and examples from our everyday lives, like Google search histories, are an eye opener.
By Amar Sahay
As the title suggests, this book is about the application of data science in business. Amar Sahay highlights how data analysis impacts the performance of a business, emphasizing predictive analysis and how it can be used to visualize future performance. The author discusses many predictive analysis models, such as forecasting, data mining and regression analysis.
Whether you own a business, work for one, or hope to in the future, you’ll find plenty of useful knowledge in this book.
By Andreas C. Müller and Sarah Guido
Sometimes simple language and basic examples are all you need to make a great book. This is the case with Andreas C. Müller and Sarah Guido’s Introduction to Machine Learning with Python. Kick-start your machine learning journey with this entry-level guide to the important concepts. By the end of it, you’ll be ready to successfully build machine models.
As machine learning becomes an increasingly crucial element of commercial applications, data scientists will benefit enormously from building their knowledge in this area. Advanced learners need not apply, but for beginners and those who already have foundational knowledge of Python, this one is highly recommended.
By Charles Wheelan
Statistics and data science go hand in hand, and this book reveals the relationship between the two in fascinating detail. Charles Wheelan explains the concepts of statistics, from basic to advanced, in a logical progression, starting out with basic topics like normal distribution before moving on to more complex ones like machine learning and data analysis.
Wheelan’s witty writing style makes for an enjoyable read, and one that will leave you with a more detailed understanding of statistics and data science.
By Viktor Mayer-Schönberger and Kenneth Cukier
Big data is no doubt one of the hottest trends in the field of data science and technology. In their best selling book, Viktor Mayer-Schönberger and Kenneth Cukier reveal the possible impacts big data will have on science, the economy, and society as a whole. If you’re looking for the most up-to-the-minute analysis on this hot topic, this book is a must-read. Whether you’re new to the field of data science or a seasoned professional, Big Data will surprise and fascinate you with its facts about how big data works.
With the latest technologies, we can collect and store as much data as we want. As Mayer-Schönberger and Cukier explain, this has led to changes in sampling and extracting methods. In order to make the most of these advancements, the next generation of data scientists will need an in-depth understanding of big data and its impacts on society. Even if you’re new to data science, the authors’ compelling arguments will make you stop and think, ultimately leaving you better informed and ready to face the future of big data.
By Charles Miller Grinstead and J. Laurie Snell
This book dives a little deeper into probability – a branch of mathematics that most readers will be familiar with. Apart from the basic concepts, you’ll learn how to use probability in correlation with data, and how it can be used across disciplines such as engineering, architecture, economics, and science. Wide-ranging examples of real-life problems make this book an enjoyable and engaging read.
With its strong foundational concepts for your data science and statistics knowledge, this is a must-have book for all the data scientists out there.
By Jiawei Han, Jian Pei, and Micheline Kamber
As any data scientist worth their salt knows, processing data is just as important as collecting the right data. This book teaches the reader all about data mining from scratch. You don’t have to be an expert or advanced level data scientist to understand the concepts set out in this book. You’ll learn data mining techniques and methods, as well as many answers to the how, when and where questions relating to data mining.
As well as helping you to learn about the latest trends and research going on in the field of data mining, this book helps you solve real-world problems with technical knowledge that gets to the heart of the issue.
Introduction to Statistical Learning
By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Statistics is the perfect tool for making sense of the vast data sets you’ll often work with as a data scientist. Introduction to Statistical Learning covers some of the most important topics of statistics such as clustering, classification, resampling methods, shrinkage approaches, and many more. The book also provides an introduction to statistical techniques in R programming.
Theoretical knowledge is one thing, but when it comes to implementing your knowledge in real-world situations, practical examples never go astray. Fortunately, each chapter of this essential text includes a tutorial about implementing the methods and analysis discussed. So whether you’re a statistician or not, this book has something to teach everybody.
Now that you’ve read our guide to the best data science books, it’s important not to become overwhelmed by this list. Make a start, go at your own pace, and you’ll soon see how each of these data-centric books can apply to your work or study.
If you’re interested in learning more about data science and boosting your career in this field, check out our tips for anyone interested in becoming a data scientist. If you’re ready to commit to learning full-time, Thinkful’s Data Science course will put you on a fast-track to securing a rewarding career path. For those needing a flexible program that allows time to hold another job, our part-time Data Science bootcamp is another option to explore. Either way, you’ll benefit from a thoroughly researched curriculum that’s designed to put you on a long-term, high growth career path in data.