Big data and data science are transforming our world today in ways we could not have imagined at the beginning of the twenty-first century. The accompanying wave of innovation has sparked advances in healthcare, engineering, business, science, and human perception, among others. The tremendous advances in computing power and intelligent techniques have opened many opportunities for managing data and investigating data in virtually every field, and the scope of data science is expected to grow over the next decade. These future research achievements will solve old challenges and create new opportunities for growth and development. Thus, the research presented in this book is interdisciplinary and covers themes embracing emotions, artificial intelligence, robotics applications, sentiment analysis, smart city problems, assistive technologies, speech melody, and fall and abnormal behavior detection. The book is directed to the researchers, practitioners, professors and students interested in recent advances in methodologies and applications of data science. An introduction to the topic is provided, and research challenges and future research opportunities are highlighted throughout.

ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student, focusing on machining learning, big data, business intelligence, and analytics. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.

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.

This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge. Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.

Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.

This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.