Best Data Science Books for Growing Your Career
Data science has dominated the business world in recent years. With the increasingly high demand for data in nearly every industry, it is not uncommon to feel overwhelmed or ill-equipped to meet expectations. Or worse: unable to distinguish between meaningful and useless data.
As you look to move up and grow your career, it's crucial to understand what data science is and how it can help companies reach their goals. Data science can help you develop a better understanding of today’s business issues, and it's especially effective for finding solutions to the most complicated business problems.
Ready to learn more? See our list of the best data science books below for professionals of any level.
Best Data Science Books for Beginners
Data Science in Practice
Alan Said and Vincenc Torra (Eds.)
This book is ideal for those wanting a general overview of the terms and approaches used in data science without the overwhelming, industry-specific language. This book has detailed information on the underlying theories, models and application scenarios. According to the publisher’s website, the book is broken up into three main parts: It addresses what data science is, how and where it is used, and how it can be implemented using modern open-source software.
An Introduction to Data Science
Jeffrey S. Saltz and Jeffrey M. Stanton
This book is another introduction to data science with input from a wide variety of backgrounds, making it helpful regardless of your industry. In later chapters of the book, the programming language R—used specifically for statistical computing and graphics—is used to help convey various concepts. Lessons and demonstrations in this book are presented with the assumption that the reader does not have prior experience in statistics and programming.
Doing Data Science: Straight Talk from the Frontline
Cathy O'Neil and Rachel Schutt
Based on the Introduction to Data Science course at Columbia University, this book features data scientists from Fortune 500 companies such as Google, Microsoft and eBay. They present algorithms, methods and models through case studies, as well as the code they have used. While considered an introduction, this book does require readers to have an understanding of linear algebra, probability, statistics, and some programming languages and logic.
Data Science: Concepts and Practice
Vijay Kotu and Bala Deshpande
This introductory book presents data science in a conceptual framework and lets readers immediately practice using RapidMiner, an open-source graphical user-interface platform. Readers can understand the concepts and inner workings of 30 commonly used and powerful data science algorithms. Besides offering various methods for extracting data for information and simple explanations for standard data science techniques, this book presents readers with practical use of data science algorithms without requiring them to know programming to understand the material.
Data Science Careers, Training, and Hiring
A different take on data science, this book is geared toward starting a career in data science and building a team of scientists. This book gives readers insights into navigating the data ecosystem along with current training programs to help hiring managers find new talent, train current talent, and help team leaders stay on top of current and emerging trends. This book offers a 360-degree view of the data science landscape.
Best Data Science Books for Intermediate Learners
The Real Work of Data Science
Ron S. Kenett and Thomas C. Redman
This book focuses on managing and guiding a team of data scientists to help make a company more data-driven. It explores common problems companies face, how to overcome quality issues and strategies for building trust with decision-makers. This book will help you capitalize on what The Economist claims to be “the world's most valuable resource.”
Programming Skills for Data Science
Michael Freeman and Joel Ross
This book will guide you through the basics of R and Git while configuring the tools you need to solve professional-level data science problems. Readers learn how to present data so that it can easily be analyzed and visualized, allowing other teams and coworkers to understand with minimal explanation. Readers can also master R programming techniques and troubleshooting skills with step-by-step instructions.
Dr. Barry Devlin
A vital part of using data to drive decisions in any company is communication between IT and the business side. This book provides various models for business and IT divisions, and helps organizations design support systems for tomorrow’s success.
Enda Ridge, PhD
When you need to show the value of data to your company but have a team of mixed skill sets and limited tools, this book can help. Learn how to maintain a reproducible analytics lifecycle from extraction to reporting. Readers get a glimpse of other issues companies have faced and overcome when working with data.
Practical Data Analysis
Build your data in a practical way with minimal resources. This book focuses on small-business owners: It offers solutions for more tech-savvy individuals to incorporate in their existing business ecosystems.
Best Data Science Books for Advanced Learners
Advanced Data Analytics Using Python
Learn more about integrating database technology such as Neo4j, Elasticsearch and MongoDB in your work. This book also offers introductions into deep learning, data frameworks, and analytic techniques such as classification, clustering, regression and forecasting.
Advanced Statistical Methods in Data Science
Ding-Geng Chen, Jiahua Chen, Xuewen Lu, Grace Y. Yi, and Hao Yu (Eds.)
This book is a compilation of presenters from the 2nd Symposium of the Institut Canadien des Sciences Statiques (Canadian Statistical Sciences Institute). The goal of the conference—and, in turn, the book—was to allow researchers to exchange ideas on statistics and data science. The chapters are expanded upon from 14 presentations from the symposium, allowing them to disseminate the findings and promote further research collaborations in this area. This book offers new methods that impact advanced statistical model development in big-data fields.
Data Science Thinking
Have you ever thought, “How does one remain competitive in the data science field?” This book presents data science thinking as a transdisciplinary field. Readers learn about the insights of data-driven research, innovation, industrialization and opportunities while addressing the needs of decision-makers who are responsible for managing data.
When program in R (or in any language), you’ll inevitably encounter issues. Whether you're a beginner, intermediate-level or even an advanced programmer, you may find yourself struggling to overcome certain problems. That is where the R Cookbook comes in: Each “recipe” within it considers a specific problem and explains a practical solution that can aid a programmer of any experience level.
Storytelling With Data: A Data Visualization Guide for Business Professionals
Cole Nussbaumer Knaflic
Once you have the data analyzed and reviewed, what do you do with it? Tell a story. Data presentations can be overwhelming (and often boring), so learning how to make data easy to understand is vital to making improvements and getting plans and budgets approved. This book teaches you to do exactly that: It breaks down the mistakes people make when presenting data and offers advice on how to make changes.
While the books discussed here might be great primers, no data science book can replace dynamic, interactive instruction from an industry expert. EmergingEd offers online foundational data science courses in machine learning that can help you build the skills and knowledge to positively impact data strategy in your organization and prepare you for a potentially lucrative career pivot.