Introducing HR Analytics with Machine Learning: Empowering Practitioners, Psychologists, and Organizations

402 Pages·2021·6.325 MB·English

by  Christopher M. Rosett, Austin Hagerty

Christopher M. Rosett and Austin Hagerty
Introducing HR Analytics with Machine
Learning
Empowering Practitioners, Psychologists, and
Organizations
1st ed. 2021
Christopher M. Rosett
Comcast Corporation, Philadelphia, PA, USA
Austin Hagerty
Microsoft Corporation, Austin, TX, USA
ISBN 978-3-030-67625-4 e-ISBN 978-3-030-67626-1
https://doi.org/10.1007/978-3-030-67626-1
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Preface
Introducing HR Analytics with Machine Learning is a book to demystify
machine learning for non-statisticians and non-data scientists as well
as to explain why and how using machine learning with employee data
(and other workforce data) requires special consideration for all
professionals, regardless of technical background. We understand that
using data to inform decisions about human capital is paramount to the
future of industry, but that very few professionals have all the domain
knowledge necessary to use data to make informed decisions across the
complex employee lifecycle. This is especially true when the insights to
inform these decisions increasingly come from the �ields of computer
science and mathematics.
This book is split into three parts:
Part I: A Model for Quality Analytics with Workforce Data
introduces the book: its purpose, the intended audience, and why
this book is relevant given today’s workforce and technology. It also
introduces a framework for analytics which we will use to
contextualize and frame machine learning throughout the text.
Part II: Bringing Science, Machine Learning, and Behavior Together
will ground readers in many of the important skills and domains
needed to conduct Machine Learning well. We will introduce (or
reintroduce) the solid-yet-basic techniques for research methods,
statistics, and computing in a way that is accessible to all skill levels
and familiarities with these topics. Part II brings these domains
together and shows how they create many of the machine learning
methodologies in use across industries today.
Part III: Getting Started with Machine Learning will, as the title
suggests, help you get started. Once we have shown the need for this
work and introduced foundational skills, we need to put the
behavioral and workforce lenses on the science and show some
practical techniques, tips, and tricks to apply machine learning in
your organization.
This part begins at the practical crossroad of using machine
learning in an applied corporate setting. As we said, there are many
unique considerations when using behavioral and employee data, and
there are many great learnings from science and history which apply to
this frontier. We will review real cases and events across psychological,
commercial, military, and other landscapes and show how some good
(and not so good) decisions have impacted people and business, and
what they can teach us about doing machine learning with workforce
data well.
Part III will also give you advice on project managing machine
learning efforts and will showcase some of the techniques you need to
begin using machine learning in partnership with your HR Analytics
teams.
We hope you enjoy Introducing HR Analytics with Machine Learning.
We want your experience with the information in this book to be
informative and impactful. And to do that with a topic such as this, we
have aimed to make the content engaging and readable with
memorable frameworks for you to store your new knowledge and
skills. We hope you can use this guide to create insights about yourself,
your organization, and your industry and keep them with you as you
continue to grow the data-based decision-making culture at your
organization. Thanks for taking this step and thanks for taking some
time to learn with us—we hope you enjoy reading the book as much as
we enjoyed writing it!
Christopher M. Rosett
Austin Hagerty
Philadelphia, PA, USA
Austin, TX, USA
Contents
Part I A Model for Quality Analytics with Workforce Data
1 Introduction
2 Analytics About Employees
3 HR Analytics Ikigai
Part II Bringing Science, Machine Learning, and Behavior
Together
4 Thinking About Your Problem-Solving Strategies
5 Great Results Come from Great Questions
6 Statistics for Non-Statisticians
7 Why Now? Computers Enable a Future with Machine Learning
8 Introducing Machine Learning
9 Common Machine Learning Techniques
Part III Getting Started with Machine Learning
10 What History Can Teach us About Using Machine Learning Well
11 Machine Learning Project Management
12 The 3 A’s of a Machine Learning Project
13 Data Wrangling
14 Bringing Your Model to Life
Afterward
Index
Part I
A Model for Quality Analytics with
Workforce Data
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
C. M. Rosett, A. Hagerty, Introducing HR Analytics with Machine Learning
https://doi.org/10.1007/978-3-030-67626-1_1