Data Science for Sensory and Consumer Scientists
eBook - ePub

Data Science for Sensory and Consumer Scientists

  1. 332 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science for Sensory and Consumer Scientists

About this book

Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences.

Key Features:

• Elucidation of data scientific workflow.

• Introduction to reproducible research.

• In-depth coverage of data-scientific topics germane to sensory and consumer science.

• Examples based in industrial practice used throughout the book

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Yes, you can access Data Science for Sensory and Consumer Scientists by Thierry Worch,Julien Delarue,Vanessa Rios De Souza,John Ennis in PDF and/or ePUB format, as well as other popular books in Economics & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Edition
1
Subtopic
Data Mining

Table of contents

  1. Cover Page
  2. Half Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication
  7. Contents
  8. Preface
  9. About the Authors
  10. 1. Bienvenue!
  11. 2. Getting Started
  12. 3. Why Data Science?
  13. 4. Data Manipulation
  14. 5. Data Visualization
  15. 6. Automated Reporting
  16. 7. Example Project: The Biscuit Study
  17. 8. Data Collection
  18. 9. Data Preparation
  19. 10. Data Analysis
  20. 11. Value Delivery
  21. 12. Machine Learning
  22. 13. Text Analysis
  23. 14. Dashboards
  24. 15. Conclusion and Next Steps
  25. Bibliography
  26. Index