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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

AI Governance

In the era of technology that we currently reside in, disruptive innovations are largely fueled by data, analytics, and AI. These elements are forging new paths across multiple sectors, birthing fresh avenues for revenue, and redefining corporate management paradigms. Consider the projection by McKinsey & Co., which forecasts an addition of over $15 trillion in business value by 2030, solely attributed to analytics and AI. In recognizing this goldmine, global organizations are fervently channeling resources to establish a strong foothold in this data-driven domain. A 2022 survey by NewVantage Partners supports this notion and indicates that a staggering 97% of surveyed entities are amplifying their investments in data-centric and AI initiatives.

However, a significant paradox emerges here. Despite the enormous capital influx, the majority of organizations grapple with extracting tangible benefits from their data endeavors. So, what seems to be the impediment? The...

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