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Book Overview & Buying
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Table Of Contents
Principles of Data Science - Third Edition
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Principles of Data Science
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Overview of this book
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.
Table of Contents (18 chapters)
Preface
Free Chapter
Chapter 1: Data Science Terminology
Chapter 2: Types of Data
Chapter 3: The Five Steps of Data Science
Chapter 4: Basic Mathematics
Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability
Chapter 6: Advanced Probability
Chapter 7: What Are the Chances? An Introduction to Statistics
Chapter 8: Advanced Statistics
Chapter 9: Communicating Data
Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials
Chapter 11: Predictions Don’t Grow on Trees, or Do They?
Chapter 12: Introduction to Transfer Learning and Pre-Trained Models
Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift
Chapter 14: AI Governance
Chapter 15: Navigating Real-World Data Science Case Studies in Action
Index
