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Essential Math for Data Science by Thomas Nield
Return to Thomas Nield, Math, Math for Data Science and DataOps, Math for Machine Learning and MLOps, Math for Programmers and Software Engineering, Outline of Mathematics, Math Bibliography, Outline of Software Engineering, Outline of Computer Science
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Essential Math for Data Science
Book Summary
“To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.”
“Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:”
- Recognize the nuances and pitfalls of probability math
- Master statistics and hypothesis testing (and avoid common pitfalls)
- Discover practical applications of probability, statistics, calculus, and machine learning
- Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added
- Perform calculus derivatives and integrals completely from scratch in Python
- Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks
About the Author
Thomas Nield is the founder of Nield Consulting Group as well as an instructor at []O'Reilly Media and University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. He's authored two books, including Getting Started with SQL (O'Reilly) and Learning RxJava (Packt).
Product Details
- Publication date: June 28, 2022
- Paperback: 350 pages
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Math: Outline of mathematics, Mathematics research, Mathematical anxiety, Pythagorean Theorem, Scientific Notation, Algebra (Pre-algebra, Elementary algebra, Abstract algebra, Linear algebra, Universal algebra), Arithmetic (Essence of arithmetic, Elementary arithmetic, Decimal arithmetic, Decimal point, numeral system, Place value, Face value), Applied mathematics, Binary operation, Classical mathematics, Control theory, Cryptography, Definitions of mathematics, Discrete mathematics (Outline of discrete mathematics, Combinatorics), Dynamical systems, Engineering mathematics, Financial mathematics, Fluid mechanics (Mathematical fluid dynamics), Foundations of mathematics, Fudge (Mathematical fudge, Renormalization), Game theory, Glossary of areas of mathematics, Graph theory, Graph operations, Information theory, Language of mathematics, Mathematical economics, Mathematical logic (Model theory, Proof theory, Set theory, Type theory, Recursion theory, Theory of Computation, List of logic symbols), Mathematical optimization, Mathematician, Modulo, Mathematical notation (List of logic symbols, Notation in probability and statistics, Physical constants, Mathematical alphanumeric symbols, ISO 31-11), Numerical analysis, Operations research, Philosophy of mathematics, Probability (Outline of probability), Statistics, Mathematical structure, Ternary operation, Unary operation, Variable (mathematics), Glossary, Bibliography (Math for Data Science and DataOps, Math for Machine Learning and MLOps, Math for Programmers and Software Engineering), Courses, Mathematics GitHub. (navbar_math - see also navbar_variables)
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