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.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
•Agglomerative Clustering
•Benefit Management
•Cancer Detection
•Change Management Science Innovation
•Global Software Development
•Industry 4.0
•Knowledge Representation
•Machine-First Incident Management
•Maintenance Prediction
•Pharmaceutical Manufacturing
•Recommendation System Analysis
•Statistical Model Selection
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.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
•Agglomerative Clustering
•Benefit Management
•Cancer Detection
•Change Management Science Innovation
•Global Software Development
•Industry 4.0
•Knowledge Representation
•Machine-First Incident Management
•Maintenance Prediction
•Pharmaceutical Manufacturing
•Recommendation System Analysis
•Statistical Model Selection