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Advances in Data Science and Analytics
Concepts and Paradigms
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Summary
The book highlights the evolving nature of these fields, detailing both foundational techniques and emerging technologies crucial for engineers, scientists, and students. It serves as a comprehensive resource on data mining, analytics, and related disciplines, illustrating how data scientists forecast trends and data analysts derive actionable insights from raw data.
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Advances in Data Science and Analytics
$37199
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?
This book is ideal for engineering professionals, data scientists, scientists, and students interested in the practical and theoretical aspects of data science and analytics. It offers valuable insights for those seeking to understand and apply advanced data-driven techniques in real-world contexts.
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Description
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 delves into the practical applications that can be utilised across multiple disciplines and industries, for both the engineer and the student. It focuses on machine 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 various structured and unstructured data. Related to data mining, deep learning, and big data, data science includes data analytics software, which is a more focused version and can even be considered part of the larger process. Analytics is devoted to realising actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science serves as 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 related areas have been around for a few decades, data science and analytics are still rapidly evolving, with processes and technologies changing almost daily. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. It is valuable as a learning tool for beginners in this area and as a daily reference for engineers and scientists working in these fields, making this a must-have for any library.
Book Details
INFORMATION
ISBN: 9781119791881
Publisher: John Wiley & Sons Inc
Format: Hardback
Date Published: 01 November 2022
Country: United States
Imprint: Wiley-Scrivener
Contributors:
- Edited by M. Niranjanamurthy
- Edited by Hemant Kumar Gianey
- Edited by Amir H. Gandomi
Audience: Professional and scholarly
DIMENSIONS
Weight: 794g
Pages: 352
About the Author
M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, India. He earned his PhD in computer science at JJTU. He has over 13 years of teaching experience and two years of industry experience as a software engineer. He has published four books and 85 papers in technical journals and conferences. He has six patents to his credit and has won numerous awards.
Hemant Kumar Gianey, PhD, is a senior assistant professor in the Computer Science Department at Vellore Institute of Technology, AP, India. He also worked at Thapar Institute of Engineering and Technology, Patiala, Punjab, India and worked as a post-doctoral researcher in the Computer Science and Engineering Department at National Cheng Kung University in Taiwan. He has over 15 years of teaching and industry experience. He has conducted many workshops and has been a guest speaker in various universities. He has also published many research papers on in scientific and technical journals.
Amir H. Gandomi, PhD, is a professor of data science in the Department of Engineering and Information Technology, University of Technology Sydney. Before joining UTS, he was an assistant professor at the School of Business, Stevens Institute of Technology, NJ, and a distinguished research fellow at BEACON Center, Michigan State University. He has published over 150 journal papers and four books and collectively has been cited more than 14,000 times. He has been named as one of the world’s most influential scientific minds and a Highly Cited Researcher (top 1%) for three consecutive years, from 2017 to 2019. He has also served as associate editor, editor, and guest editor in several prestigious journals and has delivered several keynote talks. He is also part of a NASA technology cluster on Big Data, Artificial Intelligence, and Machine Learning.
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