In the past few years public interest in data science has surged. What had been a fairly esoteric field is now a common topic in the news, in politics and international law, and in our social media feeds. Data literacy is becoming a highly desired skill in every industry, and consumers enter data points into massive business intelligence systems every day.
Whether you just want to stay informed in the data craze or you’re looking to kickstart your data science or data literacy journey, this article features a list of books that can help newcomers navigate the world of data science.
1. “The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists” by Carl Shan, William Chen, Henry Wang, and Max Song
Often the best way to get information is straight from people in the field, and what better way than to talk with 25 of the industry’s top experts? “The Data Science Handbook” interviews top leading data scientists, from the former US Chief Data Officer to team leads at prominent companies to rising data scientists creating their own programs, in order to offer a unique look into the industry.
The selection of interviews will guide newcomers through the industry, offering data life advice, learning mistakes, career development tips, and strategies to succeed in the world of data science. The book doesn’t delve into the technical aspects of the subject or try to be an all-encompassing guide. Rather, it offers a trove of practical advice and insight.
“Doing Data Science” gets straight to the point. It is based on Columbia University’s Introduction to Data Science class and is aimed at any beginners looking to make their way into the subject. Data science consultant Cathy O’Neil collaborates with course instructor Rachel Schutt to bring the data science course to the general public.
These experts not only offer knowledgeable lectures on the subject but also share relevant case studies and code, diving into accessible examples. It covers algorithms, methods, models, and data visualization, acting as a practical go-to technical resource.
Authors: Annalyn Ng and Kenneth Soo
Data science has a lot to do with math, which can make data science seem inaccessible and daunting. “Numsense” promises to deliver a math-light introduction to data science and algorithms in layman’s terms to make things less intimidating and easier to understand.
Each chapter is dedicated to a particular useful algorithm, complete with a breakdown of how it works and real-world examples to see it in use. Visuals accompany the processes to aid in understanding. Reference sheets detail the pros and cons of each algorithm and a handy glossary of common data science terms completes the book.
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.
Both authors have experience in managing data projects themselves, as well as managing analysts in a professional setting. They discuss their own experiences on what will reliably produce successful results and what pitfalls make a data project doomed to fail.
Author: Lillian Pierson
The “Dummies” series has always been adept at teaching concepts in simple terms, and “Data Science For Dummies” seeks to do the same. It focuses more on the business side of data science and acts as an introductory guide to entering the field as a professional. It’s a resource for beginners that gives a broad overview of the discipline to get readers familiar with the concepts of big data and how data science is applicable in our lives.
The book also explores broad overviews of topics like data engineering, programming languages like R and Python, machine learning, algorithms, artificial intelligence, and data visualization techniques. If you have a passing curiosity about data science, or really just want your parents to understand the gist, this might be a good place to start.
Authors: Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman
While we’re on the topic of data science for “dummies,” we also have an overview of big data and why it’s important. The book covers the central question—“What is big data?”—and explains the concept from both technical and business perspectives. It presents how big data is used in business intelligence and how it can help analysts discover and solve problems.
The book also provides technical advice on topics like how to organize and support the data you collect and how to adapt methods and tools to analyze data. “Big Data for Dummies” promises to help you figure out what your data means, what to do with it, and how to apply it in a business setting.
Author: DJ Patil
If you’re going to take advice from one person about data science, it probably wouldn’t hurt to ask a former Chief Data Scientist of United States Office of Science and Technology Policy. DJ Patil is credited for creating the term “data science” and in “Data Jujitsu,” Patil introduces data science as a mindset of problem-solving.
He highlights different issues found in data-motivated industries and notes that there’s a difference between problems that are merely difficult to solve and problems that are impossible. Complex problems can be solved by breaking them down into simplified parts and examining them with data analysis. “Data Jujitsu” covers a wide variety of examples and advice for harnessing the power of data.
8. “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schönberger and Kenneth Cukier
Author: Viktor Mayer-Schönberger and Kenneth Cukier
Big data seems like it never really leaves the news cycle. Data-first companies rise in power, data breaches and leaks of personal and banking information happen, policy debates rage, and regulations regarding data privacy become law. This book aims to discuss the effect data has on just about all aspects of our lives, from business to personal, to even the government and individual scientific disciplines.
Mayer-Schönberger and Cukier explain how algorithms can reveal things about ourselves we didn’t think anyone knew just by analyzing our habits online. Online retailers can recommend products or predict buying patterns based on browsing, social media feeds target our political biases and echo chambers. Even dating apps use data to shape love lives. As we take steps to curb what databases know about us, we also have to be careful that our data stays in the right hands. This book discusses the scary, great, and downright interesting ways our own data will—and already does—move and shape us.
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