take control of your data with fundamental linear algebra, probability, and statistics Nield, Thomas (Computer programmer) creator author. text Handbooks and manuals. Handbooks and manuals. cc 2022 First edition. monographic eng

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1 digital resource (xiv, 332 pages) ; 24 cm 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 -- Basic math and calculus review -- Probability -- Descriptive and inferential statistics -- Linear algebra -- Linear regression -- Logistic regression and classification -- Neural networks -- Career advice and the path forward. Thomas Nield. Includes index. July 5, 2022 Data mining Mathematics Machine learning Mathematics Mathematical statistics Probabilities Computer science Mathematics Computer science Mathematics Data mining Mathematics Mathematical statistics Probabilities QA76.9.D343 N54 2022 006.310151 9781098102937 1098102932 2023276388 UKMGB 230602 20250828143114.0 23168451 eng