UID:
almahu_9949560300902882
Umfang:
1 online resource (XVI, 380 p. 1 illus.)
Ausgabe:
1st ed. 2023.
Inhalt:
Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. This book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholders This is an open access book. This comprehensive volume combines relevant people analytics examples, comprehensive treatment of statistical and analytical considerations and approaches, and useful sample code into a readable and relevant package. This is an essential guide for those in the People Analytics space looking to build their analytical capabilities. - Alexis Fink, PhD, VP of People Analytics & Workforce Strategy @ Meta.
Anmerkung:
1. Getting Started -- 2. Introduction to R -- 3. Introduction to SQL -- 4. Research Design -- 5. Measurement & Sampling -- 6. Data Preparation -- 7. Descriptive Statistics -- 8. Statistical Inference -- 9. Analysis of Differences -- 10. Linear Regression -- 11. Linear Model Extensions -- 12. Logistic Regression -- 13. Predictive Modeling -- 14. Unsupervised Learning -- 15. Data Visualization -- 16. Data Storytelling.
Weitere Ausg.:
ISBN 3-031-28673-1
Sprache:
Englisch
Schlagwort(e):
Llibres electrònics
DOI:
10.1007/978-3-031-28674-2
URL:
https://doi.org/10.1007/978-3-031-28674-2
