Field Cady - Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals

1. Chapter 2
2. Chapter 6
3. Chapter 7

1. Chapter 2
2. Chapter 4
3. Chapter 5
4. Chapter 6
5. Chapter 7

Field Cady

This edition first published 2021

2021 by John Wiley & Sons, Inc.

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Library of Congress CataloginginPublication Data

Names: Cady, Field, 1984 author.

Title: Data science : the executive summary : a technical book for

nontechnical professionals / by Field Cady.

Description: Hoboken, NJ : Wiley, 2021. | Includes bibliographical

references and index.

Identifiers: LCCN 2020024708 (print) | LCCN 2020024709 (ebook) | ISBN

9781119544081 (hardback) | ISBN 9781119544166 (adobe pdf) | ISBN

9781119544173 (epub)

Subjects: LCSH: Data mining.

Classification: LCC QA76.9.D343 C33 2021 (print) | LCC QA76.9.D343

(ebook) | DDC 006.3/12dc23

LC record available at https://lccn.loc.gov/2020024708

LC ebook record available at https://lccn.loc.gov/2020024709

Cover Design: Wiley

Cover Image: monsitj/Getty Images

For my Uncle Steve, who left the world on the day this book was finished.

And for my son Cyrus, who entered shortly thereafter.

There are many data science for managers books on the market today. They are filled with business success stories, pretty visualizations, and pointers about what some of the hot trends are. That material will get you rightfully excited about data science's potential, and maybe even get you started off on the right foot with some promising problems, but it isn't enough to see projects over the finish line or bring the full benefits of data to your organization. Depending on your role you may also need to decide how much to trust a piece of analytical work, make final calls about what tools your company will invest in, and hire/manage a team of data scientists. These tasks don't require writing your own code or performing mathematical derivations, but they do require a solid grounding in data science concepts and the ability to think critically about them.

In the past, mathematical disciplines like statistics and accounting solved precisely defined problems with a clear business meaning. You don't need a STEM degree to understand the idea of testing whether a drug works or balancing a checkbook! But as businesses tackle more openended questions, and do so with datasets that are increasingly complex, the analytics problems become more ambiguous. A data science problem almost never lines up perfectly with something in a textbook; there is always a business consideration or data issue that requires some improvisation. Flexibility like this can become recklessness without fluency in the underlying technical concepts. Combine this with the fact that data science is fast becoming ubiquitous in the business world, and managers and executives face a higher technical bar than they ever did in the past.

Business education has not caught up to this new reality. Most careers follow a business track that teaches few technical concepts, or a technical track that focuses on handson skills that are useless for businesspeople. This book charts a middle path, teaching nontechnical professionals the core concepts of modern data science. I won't teach you the brass tacks of how to do the work yourself (that truly is for specialists), but I will give you the conceptual background you need to recognize good analytics, frame business needs as solvable problems, manage data science projects, and understand the ways data science is likely to transform your industry.

In my work as a consultant I have seen PMs struggle to mediate technical disagreements, ultimately making decisions based on peoples' personalities rather than the merits of their ideas. I've seen largescale proofofconcept projects that proved the wrong concept, because organizers set out inappropriate success metrics. And I've seen executives scratching their heads after contractors deliver a result, unable to see for themselves whether they got what they paid for.

Conversely, I have seen managers who can talk shop with their analysts, asking solid questions that move the needle on the business. I've seen executives who understand what is and isn't feasible, instinctively moving resources toward projects that are likely to succeed. And I've seen nontechnical employees who can identify key oversights on the part of analysts and communicate results throughout an organization.

Most books on data science come in one of two types. Some are written for aspiring data scientists, with a focus on example code and the gory details of how to tune different models. Others assume that their readers are unable or unwilling to think critically, and dumb the technical material down to the point of uselessness. This book rejects both those approaches. I am convinced that it is not just possible for people throughout the modern business workforce to learn the language of data: it is essential.