12 Best Data Science Books in 2023 | Beginner to Pro

In this article, we share the 12 best data science books in 2023. Whether you’d like to land a job as a data scientist or you want to further your data science career by learning new skills, we’ve included the most up-to-date data science books for beginners and experienced professionals.

In 2023 and beyond, data science remains essential for modern businesses that want to unlock valuable insights from their data while improving efficiency and creating innovative solutions. 

With the ability to add tremendous value, data science remains a highly lucrative field, with the Bureau of Labor Statistics reporting a median salary in excess of $100,000 for data scientists.

You may be asking, how can I learn data science? Well, alongside taking some of the best data science courses, you cannot go wrong by reading one of the best data science books. 

So if you’re ready, let’s review some of the best data science books available in 2023 to help you learn the skills you need to excel as a data scientist. 

How to Choose the Best Book for Data Science?

When looking for the best books on data science, it’s important to choose a book that aligns with your goals and learning style. Are you a hands-on learner seeking practical examples or a theoretical learner who thrives on deep understanding? As a rapidly evolving field, it’s also essential to read the most up-to-date data science books to not only stay current, but to outshine your competition.

To help you make the right choice, I've rigorously assessed the best books for data science using the following criteria:

• Publish Date: The newest data science books will likely encompass the latest tools, technologies, and methodologies.
• Length: Both concise guides and in-depth textbooks have their merits. It depends on how deep you're willing to dive.
• Rating: Peer reviews offer invaluable insight into a book’s quality.
• Formats: Availability in multiple formats (print, eBook, audiobook) caters to different learning styles.
• Content Variety: A blend of theory, practical application, real-world examples, and case studies ensure a well-rounded understanding.

Whichever data science book you choose, we’d also recommend pairing it with one of the world-class AI courses offered by Stanford. With access to thought leaders like Andrew Ng, these courses are an excellent way to complement data science skills with AI and ML.

Best Data Science Books for Beginners

1. Data Science from Scratch: First Principles with Python

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Why we chose this book

Based on our research, this data science book is a hugely valuable resource for newcomers to the field that want to delve deeper into data science and machine learning. 

The author, Joel Grus, is a research engineer at the Allen Institute for Artificial Intelligence and a former software engineer at Google. He takes readers through linear algebra, statistics, probability, and machine learning basics, all the while providing you with the necessary 'hacking' skills to kickstart your data science career. 

The book also covers cutting-edge topics like deep learning, natural language processing, and recommender systems. With a hands-on learning experience, you will learn how to implement commonly used models from scratch.

This is also an excellent book to learn about the difference between data science and machine learning while also understanding how these two fields naturally complement each other.

Features

• Python crash course included to get you up to speed
• Comprehensive coverage of foundational mathematical concepts used in data science
• Hands-on examples of collecting, cleaning, and manipulating data
• Detailed explanation and implementation of machine learning models
• Insight into natural language processing and recommender systems
• Practical applications of network analysis, MapReduce, and databases

2. A Hands-On Introduction to Data Science

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Why we chose this book

Based on our research, we found that this is one of the best data science books for beginners, as it helps to bridge the gap between theory and practice.

As an Associate Professor of Information and Computer Science, Shah effectively leverages his extensive experience in data mining and machine learning to present complex concepts in an accessible manner. 

With a focus on hands-on learning, this book offers practical examples using popular data science tools such as Python and R. From foundational concepts to real-life data science applications, this book walks you through the entire data science process. It’s no wonder that its highly praised for its clear structure, real-world examples, and thorough coverage of key data science concepts.

Features

• In-depth exploration of data science principles using Python and R
• Hands-on approach designed to bridge the gap between theory and practice
• Rich online supplements, including datasets, slides, solutions, and sample exams
• A wide range of real-life application examples, from small to big data
• Helpful insights into data collection, experimentation, and ethical considerations

3. Data Science For Dummies

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Why we chose this book

Lillian Pierson, CEO, and acclaimed data science consultant, brings her unique expertise to the fore in Data Science For Dummies.

Our findings show that this book offers an extensive tour of the data science field, catering to both novices and experts. Beginners will also appreciate the clear introduction to basic data science skills, while seasoned professionals can find value in unique data science strategies and data-monetization tactics.

The book's stand-out feature is the proprietary STAR Framework, a process that’s been proven to lead profitable data science projects. Readers praise this book for its comprehensive approach, emphasis on real-world applications, and accessible language.

Features

• Lillian Pierson's proprietary STAR Framework for leading profitable data science projects
• Insightful advice on growing a data science career
• Techniques for converting data into profit and making better business decisions
• Practical guidance on data visualization and selecting optimal data science use cases
• Strategies for building a data science strategy and monetizing data expertise
• Wide-ranging content suitable for beginners and experts alike

4. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

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Why we chose this book

Our analysis of this book shows that it’s perfect for beginners who want to master the critical mathematical concepts crucial to data science, machine learning, and statistics. This is also really useful if you need to prepare for technical data science interview questions.

Using a practical approach, Nield teaches you the essential areas of math for data science, including statistics, probability, calculus, and linear algebra. You’ll then apply these with core data science techniques like linear and logistic regression and even neural networks. 

You’ll also be introduced to some of the most useful Python libraries for data science, like NumPy and SciKit-learn, allowing you to practically explore these math concepts.

Coupled with the author's keen insights into the current state of data science and strategies for career success, this book serves as an invaluable resource for anyone seeking to hone their data science skills.

Features

• Clear explanation of key mathematical concepts using Python libraries
• In-depth coverage of techniques like linear regression, logistic regression, and neural networks in plain English
• Practical insights into data science careers and how to stand out in the job market
• Explanation of interpreting p-values and statistical significance from hypothesis testing
• Instructions on manipulating vectors and matrices and performing matrix decomposition
• Comprehensive guide on understanding the math behind the black box algorithms

5. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

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Why we chose this book

Based on our observations, this book offers a comprehensive guide to data science in the professional world. Written by award-winning data scientists Alex Gutman and Jordan Goldmeier, it aims to demystify data science and equip you with the vocabulary and tools you need to understand this field.

Uniquely, the authors focus on how to think statistically with the aim of helping you to become data-literate. With these skills, you’ll be able to understand text analytics, deep learning, and artificial intelligence, as well as how to sidestep common missteps when working with and interpreting data. These are also helpful skills you can use during a data science certification exam or peer review.

Written with a depth that remains accessible, this guide is an essential read for professionals across fields, aspiring data scientists, engineers, and executives, aiming to foster an organization-wide data mindset.

Features

• Intro to thinking statistically and understanding how variation affects decision-making
• Lessons on data literacy to help you confidently discuss statistics and results 
• Coverage of machine learning, text analytics, deep learning, and artificial intelligence
• Guidance on avoiding common pitfalls when working with and interpreting data

Best Intermediate Data Science Books

6. Data Science on the Google Cloud Platform

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Why we chose this book

Our findings show that this book encourages readers to broaden their skill set and learn both data science model creation and implementation at scale in production systems. It’s also highly praised by readers for its hands-on approach that emphasizes real-world applicability.

Written by Valliappa Lakshmanan, Director of Analytics and AI Solutions at Google Cloud, this data science book showcases how you can apply sophisticated statistical and machine learning methods to real-world problems utilizing the Google Cloud Platform (GCP). 

With a hands-on approach, the book guides you through building an end-to-end data pipeline using native tools on GCP, emphasizing best practices for scalable data and ML pipelines. If you’re keen to work with and build data science tools, this is an excellent book.

The second edition covers Cloud Run for automating and scheduling data ingest, real-time analytics with Pub/Sub and Dataflow, and employing Vertex AI for building explainable machine learning models.

Features

• Comprehensive guide on building scalable data and ML pipelines on GCP
• Instruction on automating and scheduling data ingest using Cloud Run
• Insight into creating an analytics dashboard in Data Studio
• Guidelines on real-time analytics using Pub/Sub, Dataflow, and BigQuery
• Covers Bayesian models with Spark on Cloud Dataproc & time series with BigQuery ML
• Exploration of training machine learning models and operationalizing ML with Vertex AI

7. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

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Why we chose this book

After carefully reviewing feedback from past readers, we found that this book is perfect for data professionals who seek a deeper understanding of statistical methods relevant to their field. 

Recognizing the gap in formal statistical training among many data scientists, this book offers practical guidance, illustrating how to apply statistical methods in data science and avoid common pitfalls. The second edition adds Python examples to its roster, making the book even more versatile for users familiar with either R or Python.

From exploratory data analysis, random sampling, experimental design principles, regression, and classification techniques, to machine learning methods, this book provides a comprehensive yet accessible guide aimed at practitioners with some exposure to statistics and familiarity with R and/or Python.

Features

• Comprehensive exploratory data analysis guidance for preliminary data science steps
• Lessons on how random sampling can minimize bias and enhance dataset quality
• Instructions on using regression for outcome estimation and anomaly detection
• Explanation of key classification techniques for category prediction
• Introduces statistical machine learning methods that 'learn' from data
• Insight into unsupervised learning methods for deriving meaning from unlabeled data

8. Introduction to Data Science: Data Analysis and Prediction Algorithms with R

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Why we chose this book

Written by a professor of data science and a fellow of the American Statistical Association, this data science book leverages Dr. Irizarry's vast experience in the application of statistics across various domains. 

Our research shows that it’s targeted at beginners, introducing you to various data science concepts ranging from probability, statistical inference, and linear regression to machine learning while also teaching valuable skills like R, data wrangling, and data visualization. 

The book's structure is intuitive and engaging, utilizing real-world case studies to answer specific questions through data analysis. It is broken down into six significant parts, including R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. 

Overall, it serves as an in-depth exploration of real-world data analysis challenges, with an emphasis on building a strong foundation in data science. 

Features

• A detailed introduction to R programming, data wrangling, and data visualization
• Real-world case studies for practical understanding and application of concepts
• In-depth coverage of machine learning and statistical inference with R
• Focus on productive tools like Linux shell, Git, GitHub, and document preparation

9. Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

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Why we chose this book

Our analysis shows that this is a comprehensive guide for AI and machine learning practitioners looking to harness the power of Amazon Web Services (AWS) in their data science projects. 

The authors meticulously guide you through AWS's AI and machine learning stack that synergizes data science, data engineering, and application development, demonstrating how to construct, run, and integrate pipelines into applications within minutes. 

Diving deep into real-world use cases such as natural language processing, computer vision, and fraud detection, the book provides a holistic understanding of the entire model development lifecycle and showcases ways to optimize costs and performance.

We believe it’s suitable for anyone seeking to enhance their understanding of the modern data science stack and elevate their cloud skills.

Features

• Detailed overview of the Amazon AI and ML stack for data science projects
• Real-world use case implementations, emphasizing SageMaker Autopilot
• Complete lifecycle coverage for an NLP use case
• Introduction to repeatable machine learning operations pipelines
• Insight into real-time ML, anomaly detection, and streaming with Kinesis and Kafka
• Comprehensive guide to security best practices for data science projects and workflows

Best Advanced Data Scientist Books

10. Cleaning Data for Effective Data Science 

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Why we chose this book

Our team discovered this comprehensive guide to data cleaning, which is often the pivotal first step in most data science workflows. Python expert David Mertz delivers practical and engaging lessons on how to think intelligently about data and ask the right questions using Python, R, and common command-line tools. 

By examining real and fictitious datasets, Mertz shares invaluable insights on data ingestion, anomaly detection, data quality assessment, value imputation, feature engineering, and more. 

Praised by data science experts for its practicality, detailed exercises, and comprehensive content, this is one of the best books to learn data science and a necessary resource for anyone who works with data and seeks to enhance their understanding and rigor in data hygiene.

Features

• Mastery of data cleaning techniques for real-world data science and machine learning 
• Hands-on approach with detailed exercises at the end of each chapter
• Insightful rules and heuristics for data quality assessment and bias detection
• Techniques for handling unreliable data, missing values, and engineering features
• Specific focus on time series data, de-trending, and interpolation

11. Practical Data Science with Python

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Why we chose this book

Our findings show that this book provides a deep understanding of core data science concepts through realistic and real-world examples. 

Nathan George, a data scientist with extensive teaching experience, begins with basic Python skills and slowly builds on data science techniques and Python programming methods while also focusing on ethical and privacy concerns in data science. 

You will be exposed to key Python data science packages, including pandas, SciPy, and SciKit-learn, enabling you to utilize these tools in your data science projects effectively. 

By the end of this book, you will have gained the competence to apply Python for basic data science projects and execute the data science process on any data source.

Features

• Comprehensive introduction to core data science concepts and tools in Python
• Hands-on learning approach with real-world examples and practical exercises
• Exploration of Python data science packages such as pandas, SciPy, and Scikit-learn
• Guidance on ethical and privacy concerns in data science 
• Detailed sections on data cleaning, feature engineering, data modeling, machine learning algorithms, and evaluating model performance

12. The Handbook of Data Science and AI

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Why we chose this book

Our research found that this is one of the most detailed guides for anyone wanting to understand and apply data science, AI, and Big Data. It guides readers to make informed decisions, reduce costs, and tap into new markets by effectively applying data science.

From fundamental concepts of data science, including mathematics and legal considerations, to the application of machine learning and data science tools, this book walks readers through building data platforms and generating value from these techniques.

Furthermore, it touches on current issues like natural language processing, computer vision, and modeling complex systems, ultimately empowering readers to turn experimentation into a working data science product.

Features

• Comprehensive exploration of data science fields and their practical applications
• Practical case studies demonstrating transformative effects of data science 
• Insightful guidance on turning data science experiments into working products
• Essential presentation techniques tailored for data scientists

Data Science Career Opportunities and Growth

Data science offers a wealth of career opportunities. From data scientist to machine learning engineer, the field is ripe with possibilities. Plus, it’s nice to know that the Bureau of  Bureau of Labor Statistics is projecting 36% growth for data science jobs by 2031. 

If you’re new to the field of data and data science, here are some of the most common roles:

1. Data Scientists not only perform data analysis, but they also design and implement models that use data to predict and optimize outcomes.
2. Machine Learning Engineers apply predictive models and leverage natural language processing while working with vast datasets.
3. Data Engineers prepare the "big data" infrastructure to be analyzed by data scientists.

Final Thoughts

And there you have it, the 12 best data science books to read in 2023, with a range of data science books for beginners and experienced data scientists alike.

As we continue to live in a world defined by data, data science continues to be in high demand by organizations that want to capitalize on the hidden value within their ever-evolving datasets.

By taking the time to review our recommended data science books, you should be able to find a range of data science books that align with your goals and learning style.

Whichever book you choose, we wish you luck as you continue your journey into the world of data science. 

Happy reading!

Are you new to data science and not sure where to start? Check out:

Dataquest’s Career Path for Data Science with Python

Frequently Asked Questions

1. What Is Data Science?

Data Science is an interdisciplinary field combining programming, statistical analysis, and domain expertise to extract insights from data. It uses machine learning and AI models to predict outcomes, enhance decision-making, and discover patterns in data. 

2. Which Are the Best Data Science Books?

The best data science books will vary depending on your experience level and specific interests, and we’d recommend any of the books on our list. That said, if you have little to no background, Data Science from Scratch is a friendly introduction, and if you’re more experienced, we’d recommend Practical Data Science with Python for a great hands-on guide.

3. How Can I Learn Data Science?

To learn data science, start by understanding statistics, mathematics, and programming languages such as Python or R. To get the most out of your time learning data science, consider combining online courses with one of the best data science books. We’d also recommend participating in Kaggle competitions to apply what you've learned.

4. Can 12th Graders Do Data Science?

Yes, 12th graders can begin learning data science, particularly if they're studying calculus, statistics, and programming. Learning Python, a versatile programming language used in data science, is a good start. There are resources like online tutorials and educational platforms tailored for this age group.

5. Can I Learn Data Science in One Year?

Yes, it's possible to learn the basics of Data Science in a year, but proficiency requires consistent practice. This includes learning programming languages, statistics, and machine learning algorithms and applying these skills in real-world projects. Self-study, using resources like our recommended data science books, and following a structured learning path can aid in achieving this.

6. What Book Should I Read for Data Science?

The best book to learn data science depends on your current level and specific area of interest. If you're seeking one comprehensive book for Data Science, consider Data Science from Scratch, as it offers an in-depth overview of the tools, ideas, and principles behind data science. It also includes a crash course in Python, making it a valuable asset for those starting their data science journey.

7. Is Data Science Stressful?

Data science, like any profession, can be stressful at times due to factors like tight project deadlines, data complexities, or high expectations. The role involves continuous learning, which can also feel overwhelming. However, it is often mitigated by the intellectual stimulation and satisfaction derived from solving complex problems and making impactful decisions. 

8. What Is a Data Scientist’s Salary?

The salary of a Data Scientist can vary significantly based on geographical location, years of experience, industry, and the specific role within data science. In 2023, the median base salary for a data scientist in the U.S. is over $100,000 per year. 

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