Data science is a field that focuses on analyzing data sets to draw key conclusions, using algorithms and other technological processes. Put simply, data science helps researchers find hidden patterns in raw data. According to the Bureau of Labor Statistics, data scientists are one of the fastest-growing occupations, which is why you should read the best data science books.
It’s not just tech and science jobs that use data science. Statistics and data sets lead to business insights in the finance industry and help marketers make informed decisions. A basic understanding of data science is useful no matter what industry you’re in. Keep reading to learn about the best books for data scientists, including the best books to learn Python and R.
How Can I Choose the Best Books for Data Scientists?
Data Scientists can choose the best data science books by considering their goals and where they are on their learning journey. Do you have a basic understanding of the subject already, or are you starting from scratch? If you already have a tech or data science career, you’ll want to make sure you’re picking books that cover complex topics. Keep these tips in mind while you shop.
Tips for Choosing the Best Data Science Books
- Find your niche. It’s easier to learn when you’re passionate about a subject. While a technical book can be dry, you can set yourself up for success by picking one with a range of topics you find useful or exciting.
- Research authors. A great way to evaluate a book is to research its author. Most authors have their own websites or profiles on their workplace’s website. You can read these websites to get a feel for the author’s expertise and writing style.
- Read the reviews. Product reviews on websites like Amazon are a reliable way to evaluate books. You can see the best-rated and most popular books this way, and you can even search within reviews if you have specific questions about a book.
- Ask around for recommendations. Getting personal recommendations from the data science community is an even more tried-and-true method than reading online reviews. Asking your professors, colleagues, or friends for their favorites will ensure that you end up with a high-quality book.
- Set your learning goal. If you are a more advanced learner, you should think about what specific questions and topics you want to be answered by your book. Are you looking for a reference book that you can skim through as needed, or do you need a deep dive on a specific topic? Knowing your goal will help you find the most valuable resource.
The 10 Best Data Science Books: An Overview
|The Art of Statistics: How to Learn from Data||Basic Books||Statistical literacy, statistical reasoning, real-life examples|
|Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data||Wiley||Core concepts, data mining, certification prep|
|Data Science from Scratch: First Principles with Python||O’Reilly||Python, mathematical and statistical concepts, network analysis|
|Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems||O’Reilly||Software engineering, data system architectures, deep dives into existing tech|
|Introducing Data Science: Big Data, Machine Learning, and more, using Python tools||Manning Publications||Introduction to machine learning, introductory Python, algorithms for beginners|
|Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python||O’Reilly||Exploratory data analysis, principles of experimental design, machine learning|
|Practical Data Science With R||Manning Publications||R programming language, statistical analysis, linear regression and other modeling techniques
|Python Data Science Handbook: Essential Tools for Working with Data||O’Reilly||Python, Matplotlib, scikit-learn|
|R for Data Science: Import, Tidy, Transform, Visualize, and Model Data||O’Reilly||Beginning concepts, programming skills, data modeling|
|Think Stats: Exploratory Data Analysis||O’Reilly||Probability, statistics, simulation design|
The 10 Best Data Science Books: A Closer Look
With so many books about data science on the market, it can be tough to make the right choice for your data science journey. The list of books above compiles some of the most highly recommended and popular books for aspiring data scientists and other jobs that use data science in their work. Keep reading to find out more about each title.
1. The Art of Statistics: How to Learn from Data
- Author: David Spiegelhalter
- Best for: Beginners
Before you can analyze data, work with datasets, and master data visualization, you’ll need to understand the fundamental principles of statistics. The Art of Statistics is a must-read book for anyone who uses statistics in their work, including data scientists. It covers basic statistical methods like sampling, designing experiments, and working with data.
2. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
- Authors: David Dietrich, Barry Heller, Beibei Yang, et al.
- Best for: Students with basic knowledge
The main benefit of this excellent book is its focus on preparing readers for data science certifications. By covering basic concepts such as data mining and quantitative research, this book will help build a solid foundation for those entering data science careers. There is open source companion code available on the publisher’s website as well.
3. Data Science from Scratch: First Principles with Python
- Author: Joel Grus
- Best for: Readers who want a closer look at mathematical concepts
To build from scratch as the title suggests, you need to know the nitty-gritty of how something works. Grus is not afraid to dive deep, teaching the reader about math, statistics, and hacking, as well as deep learning, natural language processing, and other core ideas in data science. This book has been updated with a Python framework for implementation.
4. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
- Author: Martin Kleppmann
- Best for: Tech workers who are new to data science
Kleppmann’s main concern is presenting information in an accessible way. This book weighs the pros and cons of different methods and systems for processing and storing data, focusing less on specific software and more on fundamental principles. It includes practical applications, open-source code examples, and a hefty bibliography for further research.
5. Introducing Data Science: Big Data, Machine Learning, and more, using Python tools
- Author: Davy Cielen, Arno D. B. Meysman, Mohamed Ali
- Best for Beginning data scientists who are familiar with coding
This book covers a wide range of tools, including Python, one of the most popular programming languages, and the variety of NoSQL technologies. The authors balance this technological focus with real-world examples, like how to keep your project’s research goals in mind while working. This is a valuable resource for anyone looking into how to become a data scientist.
6. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Author: Peter Bruce, Andrew Bruce, Peter Gedeck
- Best for: Experienced data scientists
This is a practical reference for data scientists hoping to improve their grasp of statistics. It covers topics like exploratory data analysis, random sampling, experimental design, regressions, and machine learning. An understanding of programming languages like Python and R is necessary to benefit from this comprehensive book.
7. Practical Data Science with R
- Author: Nina Zumel, John Mount, and Jim Porzak
- Best for: Beginners
This is an ideal book for absolute beginners as it covers basic principles and fundamental concepts. It uses concrete examples in easy-to-understand language and provides a code library. The book will guide you through using R and statistical analysis techniques. It also addresses experimental design and modeling techniques.
8. Python Data Science Handbook: Essential Tools for Working with Data
- Author: Jake VanderPlas
- Best for: Data science professionals
This is an essential reference that you’ll want on your desk if you work with data professionally. It covers everything, from data manipulation, data visualization, statistical methods, and machine learning models. Guidance is included for the different uses of Python, including IPython, NumPy, pandas, Matplotlib, and scikit-learn.
9. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Author: Hadley Wickham, Garrett Grolemund
- Best for: Students and professionals with programming experience
This book aims to introduce readers to the R programming language and its myriad tools that are useful for data science, including RStudio and the tidyverse. It presents an overview of the complete data science cycle, so you’ll come away with a more thorough understanding of the field. It is best for readers with some programming experience.
10. Think Stats: Exploratory Data Analysis
- Author: Allen B. Downey
- Best for: Programmers who are new to data science
If you’re a programmer who wants to increase your knowledge of statistics, this book is for you. Structured around a single case study, this book covers the whole process of exploratory data analysis and how to perform statistical analysis through programs written in Python. Readers will learn about distributions, probability, and data visualization.
Alternative Ways to Learn Data Science
While any one of these books can give you an overview of core concepts or deeper knowledge about data science, reading is not the only way to learn. Especially since programming is a practical skill, there are many people who learn better by doing, with the chance to apply their knowledge of theoretical concepts in a classroom setting.
The best data science bootcamps are a wonderful option for learning data science in a short period of time. Bootcamps are immersive settings in which attendees learn a lot of information in a short period of time. They generally have flexible options for in-person, online, and self-paced learning.
Is Learning Data Science Worth It?
Yes, learning data science is worth it. Data science is a high-earning field, with data scientists earning $100,910 per year on average according to the Bureau of Labor Statistics. Expertise in data science opens the door to many industries, including business intelligence and social media marketing. Data science is an exciting career that allows you to pursue your passion.
Best Data Science Books FAQ
To learn data science, the first step is to start learning statistics. Data science deals with big data sets, so you’ll need to learn about core concepts like data mining, aggregation, and data visualization. You’ll want to learn a coding language like Python or R. According to the TIOBE Index, Python is the most popular programming language and R is the 19th-most popular.
Yes, you can learn data science on your own. While it can be challenging, you can learn by reading books, which often come with sample code resources and practical examples for practice. You can also join self-paced bootcamps, take online classes, and watch videos to learn data science.
Yes, coding is required for data science. Python and R are widely used programming languages that are essential for working in data science. You might want to learn C++ or other programming languages as well.
The amount of time it takes to become a data scientist depends on your training method. Earning a bachelor’s degree at a university will take about four years. You can shorten the time it takes to become a data scientist by choosing to enroll in a data science bootcamp, most of which last for just a few months.
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