15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW understand and learn how to apply the math behind data science algorithms
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Hoyle, D. (2024). 15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW: understand and learn how to apply the math behind data science algorithms (1st edition.). Packt Publishing Ltd..
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Hoyle, David. 2024. 15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW: Understand and Learn How to Apply the Math Behind Data Science Algorithms. Birmingham, UK: Packt Publishing Ltd.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Hoyle, David. 15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW: Understand and Learn How to Apply the Math Behind Data Science Algorithms Birmingham, UK: Packt Publishing Ltd, 2024.
Harvard Citation (style guide)Hoyle, D. (2024). 15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW: understand and learn how to apply the math behind data science algorithms. 1st edn. Birmingham, UK: Packt Publishing Ltd.
MLA Citation, 9th Edition (style guide)Hoyle, David. 15 MATH CONCEPTS EVERY DATA SCIENTIST SHOULD KNOW: Understand and Learn How to Apply the Math Behind Data Science Algorithms 1st edition., Packt Publishing Ltd., 2024.
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Full title | 15 math concepts every data scientist should know understand and learn how to apply the math behind data science algorithms |
Author | hoyle david |
Grouping Category | book |
Last Update | 2025-01-24 12:33:29PM |
Last Indexed | 2025-05-03 03:08:42AM |
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Last Used | Feb 19, 2025 |
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505 | 0 | |a Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1: Essential Concepts -- Chapter 1: Recap of Mathematical Notation and Terminology -- Technical requirements -- Number systems -- Notation for numbers and fields -- Complex numbers -- What we learned -- Linear algebra -- Vectors -- Matrices -- What we learned -- Sums, products, and logarithms -- Sums and the notation -- Products and the notation -- Logarithms -- What we learned -- Differential and integral calculus -- Differentiation -- Finding maxima and minima -- Integration -- What we learned -- Analysis | |
505 | 8 | |a Limits -- Order notation -- Taylor series expansions -- What we learned -- Combinatorics -- Binomial coefficients -- What we learned -- Summary -- Notes and further reading -- Chapter 2: Random Variables and Probability Distributions -- Technical requirements -- All data is random -- A little example -- Systematic variation can be learned -- random variation can't -- Random variation is not just measurement error -- What are the consequences of data being random? -- What we learned -- Random variables and probability distributions -- A new concept -- random variables | |
505 | 8 | |a Summarizing probability distributions -- Continuous distributions -- Transforming and combining random variables -- Named distributions -- What we learned -- Sampling from distributions -- How datasets relate to random variables and probability distributions -- How big is the population from which a dataset is sampled? -- How to sample -- Generating your own random numbers code example -- Sampling from numpy distributions code example -- What we learned -- Understanding statistical estimators -- Consistency, bias, and efficiency -- The empirical distribution function -- What we learned | |
505 | 8 | |a The Central Limit Theorem -- Sums of random variables -- CLT code example -- CLT example with discrete variables -- Computational estimation of a PDF from data -- KDE code example -- What we learned -- Summary -- Exercises -- Chapter 3: Matrices and Linear Algebra -- Technical requirements -- Inner and outer products of vectors -- Inner product of two vectors -- Outer product of two vectors -- What we learned -- Matrices as transformations -- Matrix multiplication -- The identity matrix -- The inverse matrix -- More examples of matrices as transformations -- Matrix transformation code example | |
505 | 8 | |a What we learned -- Matrix decompositions -- Eigen-decompositions -- Eigenvector and eigenvalues -- Eigen-decomposition of a square matrix -- Eigen-decomposition code example -- Singular value decomposition -- The SVD of a complex matrix -- What we learned -- Matrix properties -- Trace -- Determinant -- What we learned -- Matrix factorization and dimensionality reduction -- Dimensionality reduction -- Principal component analysis -- Non-negative matrix factorization -- What we learned -- Summary -- Exercises -- Notes and further reading -- Chapter 4: Loss Functions and Optimization | |
520 | |a Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithms Key Features Understand key data science algorithms with Python-based examples Increase the impact of your data science solutions by learning how to apply existing algorithms Take your data science solutions to the next level by learning how to create new algorithms Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Master foundational concepts that underpin all data science applications Use advanced techniques to elevate your data science proficiency Apply data science concepts to solve real-world data science challenges Implement the NumPy, SciPy, and scikit-learn concepts in Python Build predictive machine learning models with mathematical concepts Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling Acquire mathematical skills tailored for time-series and network data types Who this book is for This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you're looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists. | ||
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