This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for todays organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, todays data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR.
This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.
Part I: Introducing Machine Learning: Past and Present.- The Historical
Lens of Sub-Fields.- The State of the People Data Industry.- Part II: The
Science, Philosophy, and Legality of using Machine Learning with People
Data.- Scientific Considerations when Working with Behavioral Data.- Legal
and Ethical Considerations when Working with Employee Data.- Part III: -
Instruction and Application of Machine Learning in an Employee Data
Context.- Introduction and Overview of Stats and Computing.- Interpret and
communicate.- Data Analyzing.- Data Wrangling.
Christopher Rosett is Senior Director of HR Analytics and Reporting at Comcast NBC Universal. He is responsible to design and drive HR analytics and reporting for 10,000+ of Comcasts technology and customer experience employees as well as support the measurement of strategic HR initiatives.
Christopher M. Rosett has experience at several Fortune 100 companies, including Comcast, Verizon, and PepsiCo. He has built expertise from within HR specializations including organizational development, talent management, learning and development, selection strategy, and HR analytics.
Christophers academic interests lie in industrial and organizational psychology, in which he has published and presented throughout his career on such topics such as person-environment fit, corporate communication strategy, building analytics functions, and machine learning in HR. He lives in the suburbs of Philadelphia with his wife and two sons.
Austin Hagerty is Director of Data and AI for Microsoft. He leads initiatives to promote growth in data science cloud usage through partnerships and delivery channels. Austin also teaches data science and cybersecurity bootcamps at the University of Texas at Austin.Austin has over twenty years of technology experience spanning a variety of roles and industries. He has extensive knowledge of workforce data and analytics, having built HR data science functions for multiple organizations. Austin has been a vocal advocate for applying machine learning techniques to solve workforce problems and improve the employee experience. Austin is an industry expert and sought-after speaker, actively presenting at and chairing conferences across the country. He resides in Austin, Texas with his wife and daughter.
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