While the digital age opened the floodgates of data, most unstructured data was largely undecipherable until new innovations allowed professionals to put the puzzle pieces together and gain valuable insights. Using data to determine efficient shipping routes, automate digital ad placement, detect cyber attacks, and improve other business processes is referred to as data science. Data scientists (and other positions that leverage data science) are in high demand, making it a solid career choice.
If you possess sharp critical thinking skills, are a problem solver, and can communicate effectively with others—and you’re willing to learn the mathematics and other hard skills required to analyze large sets of data—then you may want to consider a career in data science. You can check out our Data Science Bootcamp to help you get started on your journey. Even if you’re not planning to become a data scientist, supplemental knowledge of the field can be applied to numerous roles within an organization.
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The Difference Between Data Science and Data Analytics
While people often use the terms interchangeably, there is a significant difference between data science and data analytics. The field of data analytics combines sets of data to make insightful findings that can help organizations reach their goals. Data science, on the other hand, connects information and data points to find relationships that may turn out to be useful for the business. This means there is also a significant difference between a data analyst and a data scientist, although there is some overlap.
Best Data Science Books to Read in 2023
You can get a head start right now by reading some of the latest data science books on the market. We’ll discuss the best data science books available so you can add them to your 2021 reading list and get up to speed on the data science revolution.
1. Essential Math for Data Science: Calculus, Statistics, Probability Theory, and Linear Algebra, by Hadrien Jean
While it is possible to get into data science without fully understanding mathematics at its core, a truly effective and versatile data scientist should have a solid foundation in math. Hadrien Jean’s Essential Math for Data Science strives to explain the mathematics at the core of data science, machine learning, and deep learning. Whether you’re a data scientist lacking a mathematical background or a developer hoping to add data analysis to your toolkit, this book will help you expand your data science capabilities through mathematical fluency.
Essential Math for Data Science book also demonstrates how Python and Jupyter may be leveraged for plotting data and visualizing space transformations, and covers machine learning libraries such as TensorFlow and Keras.
2. A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills (2nd Edition), by Jay Wengrow
This practical, hands-on guide to data structures and algorithms goes beyond theory and will help you vastly improve your programming skills. By reading this Data Science book You’ll learn how to use hash tables, trees, and graphs to improve the efficiency of your code, with practical exercises in each chapter so you can practice what you have learned before moving on to the next topic. While algorithms and data structures are often presented as theoretical concepts, this book focuses on mastering these concepts so that you can make your code run faster and more efficiently.
3. Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects, by Neal Fishman, Cole Stryker, and Grady Booch
In a business environment, data science is too often pushed into a corner and doesn’t always make its presence felt where it’s needed the most. Even the best, most well-trained data scientists won’t get very far in their careers if they aren’t able to make an impact on the rest of the organization. Smarter Data Science book addresses these shortcomings by exploring the reasons data science projects often fail at the enterprise level, and how to fix them.
This Data Science book is designed to help directors, managers, IT professionals, and analysts effectively scale their data science programs so they’re predictable, repeatable, and ultimately benefit the entire organization. You’ll learn how to both create valuable data science initiatives and effectively get everyone on board at your organization.
4. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (2nd Edition), by Peter Bruce, Andrew Bruce, and Peter Gedeck
This Data Science book helps current and aspiring data scientists who lack formal training in statistics to master its fundamentals. While Practical Statistics for Data Scientists gets back to basics, it does so from the perspective of data science, so you can learn how to apply statistical methods to your day-to-day tasks. The recently published second edition provides examples of statistical applications in Python and highlights the statistical concepts that are (and aren’t) important for data scientists to learn.
5. Data Science for Beginners, by Andrew Park
If you’re completely new to data science, then this four-book set for beginners is for you. Together, these data science books provide a solid basic understanding of Python, data analysis, and machine learning. Each book provides step-by-step instructions and tutorials on how to leverage the popular Python programming language to create neural networks, manipulate data, and master the basics. Data Science for Beginners consists of the following books:
- Python for Beginners
- Python for Data Analysis
- Python Machine Learning
- Python Data Science
6. Build a Career in Data Science, by Emily Robinson and Jacqueline Nolis
Understanding the fundamental mathematics, theories, and technologies that comprise data science is not the same as preparing for a career. As the title states, Build a Career in Data Science is more of a career guide than a standard Data Science book. The authors set out to fill in the gaps between academia and landing your first job (or advancing in your current data science career). This Data science book covers such topics as the lifecycle of a typical data science project, how to adapt to company needs, how to prepare for a management role, and even tips to help you manage difficult stakeholders.
7. Data Science for Dummies (2nd Edition), by Lillian Pierson
While Data Science for Dummies (published in 2017) isn’t exactly new, it’s still a great introduction to the field for newcomers. Lillian Pierson’s Data Science book covers the basics that you’ll need to know as a data scientist, including MPP platforms, Spark, machine learning, NoSQL, Hadoop, big data analytics, MapReduce, and artificial intelligence. The title may be a bit of a misnomer, as its intended readers are IT professionals and technology students. It’s not a hands-on instructional guide, but rather a solid overview of data science that makes the complex field more approachable.
Take Your Data Science Knowledge to the Next Level: Get Certified
Reading the best Data Science books is a smart way to get acquainted with the subject or sharpen your skills, but nothing can replace the effectiveness of a classroom education. Simplilearn offers several training and certification options that you can access online from anywhere, which offer the advantages of live, instructor-led classes along with self-paced tutorials. For instance, our Data Science Certification, in collaboration with IBM, provides a world class education that will help you become career-ready upon completion. Get started today!
