Whether you’re brand new to the world of data or an experienced veteran, there’s always something new to learn. Here we have compiled a selection of books on data for every skill level, whether beginner, intermediate, or advanced, as well as a bonus book that may be interesting for any data connoisseur. While there’s no single “best” book or resource for learning more about data, these provide a large cross-section of topics to explore within the world of big data.
Newbie: Data books for beginners
Authors: Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman
For absolute beginners looking into approaching a subject, the “Dummies” series does a fine job of introducing information in an easy to understand way without being overwhelming. It presents a broad overview of Big Data (with and without the capital B and D) and how it can be useful for business success. “Big Data for Dummies” explains what Big Data is and how to handle the data you collect.
Author: Thomas H. Cormen
“Algorithms Unlocked” presents algorithms in real-world contexts, revealing the underlying workings of everyday technology show how algorithms can accomplish things we take for granted. When we think of algorithms, we tend to imagine esoteric math, huge “Beautiful Mind” chalkboards, and mad-genius savants. But “Algorithms Unlocked” shows tangible examples of how algorithms function without the esoteric. This overview walks anyone through how computers use algorithms to solve problems, creating the technology that seems like it works through magic.
This book is a resource for anyone who is brand new to data science. It explains algorithms in real-world applications easy for beginners to relate to concepts they are familiar with. The examples are broken down and analyzed to unlock the mystery of algorithm development.
Authors: Roger D. Peng and Elizabeth Matsui
“The Art of Data Science” dives into the practice of exploring and finding discoveries within any lake of data at your fingertips. It focuses on the process of analyzing data and filtering it down to find the underlying stories. The authors use their own experiences to coach both beginners and managers through analyzing data science.
This is an introduction to the field of data science for anyone looking to get started, whether they’re a student in school or a manager, looking to harness the power of data for business.
Intermediate: Data books for growing enthusiasts
Authors: Robert Sedgewick and Kevin Wayne
“Algorithms” is a comprehensive look at algorithms and data structure, intended to provide mastery of the subject. It’s great for intermediate readers in that readers will be able to move faster through the information with they have a solid foundational data knowledge.
The book teaches readers how to sort through data, analyze it, and process different data types. It not only teaches how to code and implement algorithms, but what to do with the data it collects and how it is stored in data structures. Along with the physical book, it also comes with a companion website for coding practice and test data.
Authors: Drew Conway and John Myles White
For those with a programming background, “Machine Learning for Hackers” is a good way to get started in the machine learning side of data. Rather than focusing on the mathematical theory behind the subject, the book presents hands-on case studies to show how machine learning works in practice. It also presents typical problems in machine learning and common solutions using the R programming language, which most programmers will already be familiar with.
“Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett
Authors: Foster Provost and Tom Fawcett
For those looking to incorporate data science into your business, what better way than to consult with two of the industry’s top experts? In this book, Foster Provost and Tom Fawcett go over the fundamentals of data science and dive into the details of this emerging industry.
The book goes over the current data-mining techniques used to collect user data and how businesses can make use of it. It offers advice on adjusting mindsets to tune into data analytics and understand how to leverage data as an investment to make business decisions. “Data Science for Business” will give readers the tools and understanding needed to aid their business in this growing field.
Advanced: Data books for ever-learning pros
Author: Peter Brass
“Advanced Data Structures” is exactly what it claims to be—an advanced guide to data types and structures. There is very little focus on algorithm development, rather it delves into the complexities of data storage within data analysis. Programmers with an understanding of the basics of algorithms will most benefit from this text.
The book presents a variety of data structures, discussing the various forms like stacks, queues, and hash tables, while even diving into more specialized structures like interval trees. Code examples in C are sprinkled throughout the lessons to show it in practice. This text isn’t for the faint of heart. Its complexity, along with its textbook price, makes it primarily a resource for more serious readers.
Author: Barry Devlin
Business intelligence has long been changing. Where once it was relatively simple, these days new technologies have caused rapid evolution. It’s enough for any business owner to kind of scratch their head wondering what to do. In “Business unIntelligence,” Dr. Barry Devlin explores the challenges many businesses face in trying to keep up with internet data, social media, and mobile users. He details the timeline that has brought us here and what within the data models is changing.
Devlin challenges previously held assumptions and best practices, suggesting new models to support current business decisions and accommodate the changing field. This book is great for those who are familiar with business intelligence and are open to scrutinizing old methods and learning new ones.
Bonus book for any level
“The Signal and the Noise” is an interesting lesson on the subject of both big data’s capabilities and limitations. Nate Silver is known for harnessing the power of data to predict outcomes of popular world events in culture and politics. Starting small with baseball performance, he soon rose to notoriety by combining polling data with historical voting trends to predict two national elections before the votes were cast.
In this book, Silver strives to find the truth from the flood of data. There is so much data out there that certain tools and deep analysis are needed to sift through the noise to find reality. When forecasters become overconfident or loosen their methods, predictions can backfire. This book deals with more of the human element than most other books on data science, it’s an interesting look at how people look for and discover information.
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