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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 (17 chapters)
Preface
Free Chapter
1. How to Sound Like a Data Scientist
2. Types of Data
3. The Five Steps of Data Science
4. Basic Mathematics
5. Impossible or Improbable - A Gentle Introduction to Probability
6. Advanced Probability
7. Basic Statistics
8. Advanced Statistics
9. Communicating Data
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
11. Predictions Don't Grow on Trees - or Do They?
12. Beyond the Essentials
13. Case Studies
14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service
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Index
