Data science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers.
Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you’ll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems.
By the end of the book, you’ll have the confidence to apply key mathematical concepts to your data science challenges.

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Spis treści

15 Math Concepts Every Data Scientist Should Know. Understand and learn how to apply the math behind data science algorithms eBook -- spis treści

• 1. Recap of Mathematical Notation and Terminology
• 2. Random Variables and Probability Distributions
• 3. Matrices and Linear Algebra
• 4. Loss Functions and Optimization
• 5. Probabilistic Modeling
• 6. Time Series and Forecasting
• 7. Hypothesis Testing
• 8. Model Complexity
• 9. Function Decomposition
• 10. Network Analysis
• 11. Dynamical Systems
• 12. Kernel Methods
• 13. Information Theory
• 14. Non-Parametric Bayesian Methods
• 15. Random Matrices