Building an effective data science practice a framework to bootstrap and manage a successful data science practice /

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Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analyst...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Raina, Vineet (Author), Krishnamurthy, Srinath (Author)
Format: Electronic eBook
Language:Inglés
Published: [California] : Apress, 2022.
Subjects:
Data mining.
Big data.
Machine learning.
Business > Data processing.
Data Mining
Machine Learning
Exploration de données (Informatique)
Données volumineuses.
Apprentissage automatique.
Gestion > Informatique.
Big data
Business > Data processing
Data mining
Machine learning
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • Part One: Fundamentals
  • 1. Introduction: The Data Science Process
  • 2. Data Science and your business
  • 3. Monks vs. Cowboys: Data Science Cultures
  • Part Two: Classes of Problems
  • 4. Classification
  • 5. Regression
  • 6. Natural Language Processing
  • 7. Clustering
  • 8. Anomaly Detection
  • 9.Recommendations
  • 10. Computer Vision
  • 11. Sequential Decision Making
  • Part Three: Techniques & Technologies
  • 12. Overview
  • 13. Data Capture
  • 14. Data Preparation
  • 15. Data Visualization
  • 16. Machine Learning
  • 17. Inference
  • 18. Other tools and services
  • 19. Reference Architecture
  • 20. Monks vs. Cowboys: Praxis
  • Part Four: Building Teams and Executing Projects
  • 21. The Skills Framework
  • 22. Building and structuring the team
  • 23. Data Science Projects
  • Appendix FAQs.

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