Data Science For Dummies - Lillian Pierson

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      This book was written as much for expert data scientists as it was for aspiring ones. Its content represents a new approach to doing data science — one that puts business vision and profitably at the heart of our work as data scientists.

      Data science and artificial intelligence (AI, for short) have disrupted the business world so radically that it's nearly unrecognizable compared to what things were like just 10 or 15 years ago. The good news is that most of these changes have made everyone’s lives and businesses more efficient, more fun, and dramatically more interesting. The bad news is that if you don’t yet have at least a modicum of data science competence, your business and employment prospects are growing dimmer by the moment.

      Since 2014, when this book was first written (throughout the first two editions), I have harped on this same point. Lots of people listened! So much has changed about data science over the years, however, that this book has needed two full rewrites since it was originally published. What changed? Well, to be honest, the math and scientific approach that underlie data science haven’t changed one bit. But over the years, with all the expansion of AI adoption across business and with the remarkable increase in the supply of data science workers, the data science landscape has seen a hundredfold increase in diversity with respect to what people and businesses are using data science to achieve.

      The original idea behind this book when it was first published was to provide “a reference manual to guide you through the vast and expansive areas encompassed by data science.” At the time, not too much information out there covered the breadth of data science in one resource. That has changed!

      Data scientist as a title only really began to emerge in 2012. Most of us practitioners in the field back then were all new and still finding our way. In 2014, I didn’t have the perspective or confidence I needed to write a book like the one you're holding. Thank you so much to all the readers who have read this book previously, shared positive feedback, and applied what they learned to create better lives for themselves and better outcomes for their companies. The positive transformation of my readers is a big part of what keeps me digging deep to produce the very best version of this book that I possibly can.

      I also want to make three further promises about the content in this book: It is meaningful, it is actionable, and it is relevant. If it isn’t one of these three adjectives, I’ve made sure it hasn’t made its way into this book.

      Because this book is about data science, I spend the entirety of Parts 1 and 2 detailing what data science actually is and what its theoretical underpinnings are. Part 3 demonstrates the ways you can apply data science to support vital business functions, from finance to marketing, from decision support to operations. I’ve even written a chapter on how to use data science to create what may be a whole new function within your company: data monetization. (To ensure that the book’s content is relevant to readers from all business functions and industries, I’ve included use cases and case studies from businesses a wide variety of industries and sectors.)

      To enhance the relevance of this book’s content, at the beginning of the book I guide readers in a self-assessment designed to help them identify which type of data science work is most appropriate for their personality — whether it’s implementing data science, working in a management and leadership capacity, or even starting your own data science business.

      Part 4 is the actionable part of this book — the part that shows you how to take what you’ve learned about data science and apply it to start getting results right away. The action you learn to take in this book involves using what you learn about data science in Parts 1 through 3 to build an implementation plan for a profit-forming data science project.

      Throughout this book, you’ll find references to ancillary materials that directly support what you’re learning within these pages. All of these support materials are hosted on the website that companions this book, http://businessgrowth.ai/. I highly recommend you take advantage of those assets, as I have donated many of them from an archived, limited-edition, paid product that was only available in 2020.

      Note: I have removed all coding examples from this book because I don’t have adequate space here to do anything meaningful with coding demos. If you want me to show you how to implement the data science that’s discussed in Part 2, I have two Python for Data Science Essential Training courses on LinkedIn Learning. You’re most welcome to follow up by taking those courses. You access them both directly through my course author page on LinkedIn Learning here: www.linkedin.com/learning/instructors/lillian-pierson-p-e

      Helping readers like you is my mission in life!

      In keeping with the For Dummies brand, this book is organized in a modular, easy-to-access format that allows you to use the book as an owner’s manual. The book’s chapters are structured to walk you through a clear process, so it’s best to read them in order. You don’t absolutely have to read the book through, from cover to cover, however. You can glean a great deal from jumping around, although now and then you might miss some important context by doing so. If you’re already working in the data science space, you can skip the basic-level details about what data science is within Part 2 — but do read the rest of the book, because it’s designed to present new and immensely valuable knowledge for data science practitioners of all skill levels (including experts).

      Web addresses appear in monofont. If you’re reading a digital version of this book on a device connected to the Internet, you can click a web address to visit that website, like this: www.dummies.com.

      In writing this book, I’ve assumed that readers are comfortable with advanced tasks in Microsoft Excel — pivot tables, grouping, sorting, plotting, and the like. Having strong

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