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.

Table of Contents

Chapter 1 Implementation Tools for Generating Statistical Consequence Using Data Visualization Techniques

Chapter 2 Decision Making and Predictive Analysis for Real Time Data

Chapter 3 Optimizing Water Quality with Data Analytics and Machine Learning

Chapter 4 Lip Reading Framework using Deep Learning and Machine Learning

Chapter 5 New Perspective to Management, Economic Growth and Debt Nexus Analysis: Evidence from Indian Economy

Chapter 6 Data-Driven Delay Analysis with Applications to Railway Networks

Chapter 7 Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co-Occurrence Matrix, and Convolutional Neural Network (CNN)

Chapter 8 loT Technologies for Smart Healthcare

Chapter 9 Enhancement of Scalability of SVM Classifiers for Big Data

Chapter 10 Electrical Network-Related Incident Prediction Based on Weather Factors

Chapter 11 Green loT: Environment-Friendly Approach to loT

Chapter 12 Big-Data Analytics: A New Paradigm Shift in Micro Finance Industry

Chapter 13 Big Data Storage and Analysis

Chapter 14 A Framework for Analysing Social Media and Digital Data by Applying Machine Learning Techniques for Pandemic Management

About the Authors

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.