Radosław Antoni Kycia – författare

Generation Z (Gen Z) is the young generation born between the mid-1990s and 2010s. They are now entering the market and starting their first jobs. Therefore, managers must shape the company workplace environment to encourage young employees to work efficiently and connect their future with the company. Only then will both managers and employees share mutual satisfaction from collaboration and aim at the common target, which should be the prosperity of the company. This book presents research results and techniques for analysing the working expectations and needs of Gen Z. The analyses were made in various countries in Europe: The Czech Republic, Latvia, Poland, and Portugal.The book contains chapters that present the analysis results and technical chapters that outline modern methods of analysis of management data, including tutorial chapters on machine learning, which currently makes a strong appearance in research in various disciplines. This volume will be of interest to researchers, academics, practitioners, and students in the fields of management studies, research methods, and human resource management.The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license.

Generation Z (Gen Z) is the young generation born between the mid-1990s and 2010s. They are now entering the market and starting their first jobs. Therefore, managers must shape the company workplace environment to encourage young employees to work efficiently and connect their future with the company. Only then will both managers and employees share mutual satisfaction from collaboration and aim at the common target, which should be the prosperity of the company. This book presents research results and techniques for analysing the working expectations and needs of Gen Z. The analyses were made in various countries in Europe: The Czech Republic, Latvia, Poland, and Portugal.The book contains chapters that present the analysis results and technical chapters that outline modern methods of analysis of management data, including tutorial chapters on machine learning, which currently makes a strong appearance in research in various disciplines. This volume will be of interest to researchers, academics, practitioners, and students in the fields of management studies, research methods, and human resource management.The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license.

Introduction to Mathematical Modeling and Computer Simulations, Second Edition continues to serve as an engaging and accessible textbook for undergraduates studying mathematical modeling and computer simulations. The book is heavily focussed on applications, and so may have a particular appeal to applied mathematicians, engineers, and others working in applied quantitative disciplines. The book may also be useful as a reference text for reference text for early-career stage practitioners. New to this Edition:A new chapter on Machine Learning and Data Analysis in order to account for recent developments in the field.Chapter 9, ‘Asymptotic Methods in Composites’, has been entirely re-written to make it more consistent with industry and scientific standards.Includes an elementary introduction to programming in Python language.The Jupyter notebooks with examples for Chapter 10 and Appendix A are available for a download from www.Routledge.com/9781032661513.

Introduction to Mathematical Modeling and Computer Simulations, Second Edition continues to serve as an engaging and accessible textbook for undergraduates studying mathematical modeling and computer simulations. The book is heavily focussed on applications, and so may have a particular appeal to applied mathematicians, engineers, and others working in applied quantitative disciplines. The book may also be useful as a reference text for reference text for early-career stage practitioners. New to this Edition: A new chapter on Machine Learning and Data Analysis in order to account for recent developments in the field. Chapter 9, 'Asymptotic Methods in Composites', has been entirely re-written to make it more consistent with industry and scientific standards. Includes an elementary introduction to programming in Python language. The Jupyter notebooks with examples for Chapter 10 and Appendix A are available for a download from www.Routledge.com/9781032661513.