Includes bibliographical references and index.

Chapter 1 Introduction to Data Science: Review, Challenges and Opportunities -- Chapter 2 Recommender Systems: Challenges and Opportunities in the Age of Big Data and Artificial Intelligence -- Chapter 3 Machine Learning for Data Science Applications -- Chapter 4 Classification and Detection of Citrus Diseases using Deep Learning -- Chapter 5 Credibility Assessment of Healthcare Related Social Media Data -- Chapter 6 Filtering and Spectral Analysis of Time Series Data: A Signal Processing Perspective and Illustrative Application to Stock Market Index Movement Forecasting -- Chapter 7 Data Science in Education -- Chapter 8 Spectral characteristics and behavioral analysis of deep brain stimulation by the nature-inspired algorithm -- Chapter 9 Visual Question Answering system using integrated models of image captioning and BERT -- Chapter 10 Deep Neural Networks for Recommender Systems -- Chapter 11 Application of Data Science in Supply Chain Management: Real-world Case Study in Logistics -- Chapter 12 A CaseStudy on Disease Diagnosis using Gene Expression Data Classification with Feature Selection : Application of Data Science Techniques in Healthcare -- Chapter 13 Case Studies in Data Optimization using Python -- Chapter 14 Deep Parallel-Embedded BioNER Model for Biomedical Entity Extraction -- Chapter 15 Predict the Crime Rate against Women using Machine Learning Classification Techniques -- Chapter 16 Page Rank Based Extractive Text Summarization -- Chapter 17 Scene Text Analysis.

The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications.