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Download this book in **EPUB** and **PDF** formats

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- Learn practical data science combined with data theory to gain maximum insights from data
- Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models
- Explore actionable case studies to put your new skills to use immediately
- Purchase of the print or Kindle book includes a free PDF eBook

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.
Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.
With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.
By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

- Master the fundamentals steps of data science through practical examples
- Bridge the gap between math and programming using advanced statistics and ML
- Harness probability, calculus, and models for effective data control
- Explore transformative modern ML with large language models
- Evaluate ML success with impactful metrics and MLOps
- Create compelling visuals that convey actionable insights
- Quantify and mitigate biases in data and ML models

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Preface

1. Chapter 1: Data Science Terminology

2. Chapter 2: Types of Data

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

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