Data science and its applications
Author(s)
Bibliographic Information
Data science and its applications
（A Chapman & Hall book）
CRC Press, 2021
- : hbk
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"
