Data Science and Its Applications
eBook - ePub

Data Science and Its Applications

  1. 408 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science and Its Applications

About this book

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.

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Yes, you can access Data Science and Its Applications by Aakanksha Sharaff, G R Sinha, Aakanksha Sharaff,G R Sinha in PDF and/or ePUB format, as well as other popular books in Computer Science & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Table of Contents
  7. Preface
  8. Acknowledgements
  9. Editor Biographies
  10. List of Contributors
  11. Chapter 1: Introduction to Data Science: Review, Challenges, and Opportunities
  12. Chapter 2: Recommender Systems: Challenges and Opportunities in the Age of Big Data and Artificial Intelligence
  13. Chapter 3: Machine Learning for Data Science Applications
  14. Chapter 4: Classification and Detection of Citrus Diseases Using Deep Learning
  15. Chapter 5: Credibility Assessment of Healthcare Related Social Media Data
  16. Chapter 6: Filtering and Spectral Analysis of Time Series Data: A Signal Processing Perspective and Illustrative Application to Stock Market Index Movement Forecasting
  17. Chapter 7: Data Science in Education
  18. Chapter 8: Spectral Characteristics and Behavioral Analysis of Deep Brain Stimulation by the Nature-Inspired Algorithms
  19. Chapter 9: Visual Question-Answering System Using Integrated Models of Image Captioning and BERT
  20. Chapter 10: Deep Neural Networks for Recommender Systems
  21. Chapter 11: Application of Data Science in Supply Chain Management: Real-World Case Study in Logistics
  22. Chapter 12: A Case Study on Disease Diagnosis Using Gene Expression Data Classification with Feature Selection: Application of Data Science Techniques in Health Care
  23. Chapter 13: Scene-Text Analysis
  24. Chapter 14: Deep Parallel-Embedded BioNER Model for Biomedical Entity Extraction
  25. Chapter 15: Predict the Crime Rate Against Women Using Machine Learning Classification Techniques
  26. Chapter 16: PageRank–Based Extractive Text Summarization
  27. Chapter 17: Scene-Text Analysis
  28. Index