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Principles of Data Science

You're reading from   Principles of Data Science A beginner's guide to essential math and coding skills for data fluency and machine learning

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837636303
Length 326 pages
Edition 3rd Edition
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Data Science Terminology 2. Chapter 2: Types of Data FREE CHAPTER 3. Chapter 3: The Five Steps of Data Science 4. Chapter 4: Basic Mathematics 5. Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability 6. Chapter 6: Advanced Probability 7. Chapter 7: What Are the Chances? An Introduction to Statistics 8. Chapter 8: Advanced Statistics 9. Chapter 9: Communicating Data 10. Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials 11. Chapter 11: Predictions Don’t Grow on Trees, or Do They? 12. Chapter 12: Introduction to Transfer Learning and Pre-Trained Models 13. Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift 14. Chapter 14: AI Governance 15. Chapter 15: Navigating Real-World Data Science Case Studies in Action 16. Index 17. Other Books You May Enjoy

A guide to architectural governance

Architectural governance is the cornerstone of ensuring the seamless integration of IT infrastructure, and it supports core business processes. Its principal objectives encompass the following:

  • Cataloging current architectural layouts
  • Establishing guidelines, principles, and benchmarks
  • Aligning business and IT visions
  • Crafting a target infrastructure blueprint
  • Identifying the value proposition of the target framework
  • Highlighting disparities between the current and desired architecture
  • Crafting a comprehensive architectural roadmap

The five pillars of architectural governance

With these five pillars in mind, we can also turn to some principles on how to modernize your governance architecture and optimize it's effectiveness.

  • Consistency: Ensuring harmonious and integrated workflows without hitches
  • Security: This is paramount for protecting sensitive data and upholding regulatory compliance...
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