Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

Featuring state-of-the-art research from leading academics in technology and organization studies, this timely Research Handbook provides a comprehensive overview of how AI becomes embedded in decision making in organizations, from the initial considerations when implementing AI to the use of such solutions in strategic decision making.

Identifying athletic talent and developing that talent to its full potential is a central concern in sport. Understanding talent identification and its implications for both positive and negative developmental outcomes is crucial to sporting success. This is the first comprehensive resource for scientists, researchers, students, coaches, analysts and policymakers looking to improve their knowledge of the talent identification and development process. With contributions from leading researchers and practitioners, this book offers a complete overview of contemporary talent identification and development from in-depth discussion of methodological and philosophical issues through to practical applications. Adopting an international and multi-disciplinary approach, it addresses all key aspects of the talent identification and development process, including skill acquisition and motor learning, psychological factors and family influences, creating optimal environments for performance, and dealing with injury and rehabilitation. Presenting an unrivalled wealth of research, the Routledge Handbook of Talent Identification and Development in Sport is an essential resource for any undergraduate or postgraduate degree course in sport studies, sport science, sport coaching or sport management, as well as for sport policymakers, analysts and coaches.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

The SAGE Handbook on Decision Making, Assessment and Risk in Social Work provides a comprehensive overview of key debates and issues; and gives pointers on future directions for practice, research, teaching, management of services, and development of theoretical understandings. It′s key aim is to support the development of sound, applied ‘science’ (knowledge) and values to underpin reasoned, reasonable professional judgement and decision making by social workers in practice and those in management and regulatory roles.

Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies.

Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.

These proceedings represent the work of contributors to the 24th European Conference on Knowledge Management (ECKM 2023), hosted by Iscte – Instituto Universitário de Lisboa, Portugal on 7-8 September 2023. The Conference Chair is Prof Florinda Matos, and the Programme Chair is Prof Álvaro Rosa, both from Iscte Business School, Iscte – Instituto Universitário de Lisboa, Portugal. ECKM is now a well-established event on the academic research calendar and now in its 24th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. The opening keynote presentation is given by Professor Leif Edvinsson, on the topic of Intellectual Capital as a Missed Value. The second day of the conference will open with an address by Professor Noboru Konno from Tama Graduate School and Keio University, Japan who will talk about Society 5.0, Knowledge and Conceptual Capability, and Professor Jay Liebowitz, who will talk about Digital Transformation for the University of the Future. With an initial submission of 350 abstracts, after the double blind, peer review process there are 184 Academic research papers, 11 PhD research papers, 1 Masters Research paper, 4 Non-Academic papers and 11 work-in-progress papers published in these Conference Proceedings. These papers represent research from Australia, Austria, Brazil, Bulgaria, Canada, Chile, China, Colombia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Iran, Iraq, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Kuwait, Latvia, Lithuania, Malaysia, México, Morocco, Netherlands, Norway, Palestine, Peru, Philippines, Poland, Portugal, Romania, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Tunisia, UK, United Arab Emirates and the USA.

Fog computing is quickly increasing its applications and uses to the next level. As it continues to grow, different types of virtualization technologies can thrust this branch of computing further into mainstream use. The Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science is a key reference volume on the latest research on the role of next-generation systems and devices that are capable of self-learning and how those devices will impact society. Featuring wide-ranging coverage across a variety of relevant views and themes such as cognitive analytics, data mining algorithms, and the internet of things, this publication is ideally designed for programmers, IT professionals, students, researchers, and engineers looking for innovative research on software-defined cloud infrastructures and domain-specific analytics.

Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key Features Master the concepts of statistics and ML to interpret models and guide decisions Identify valuable AI use cases and manage data science projects from start to finish Empower top data science teams to solve complex problems and build AI products Purchase of the print Kindle book includes a free PDF eBook Book DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.What you will learn Discover how to interpret common statistical quantities and make data-driven decisions Explore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learning Find out how to evaluate statistical and machine learning models Understand the data science lifecycle, from development to monitoring of models in production Know when to use ML, statistical modeling, or traditional BI methods Manage data teams and data science projects effectively Who this book is for This book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.