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Book Overview & Buying
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Table Of Contents
Principles of Data Science - Second Edition
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Principles of Data Science
By:
Overview of this book
Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas.
Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.
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
