03457cam a22005657i 4500 23168451 OSt 20250828143114.0 230602s2022 cc 001 0 eng d 2023276388 GBC290257 bnb 020621576 Uk 9781098102937 paperback 1098102932 paperback 23168451 (OCoLC)1328015167 UKMGB eng rda UKMGB FIE OCLCF UAP NVC JRZ OCL VNVGU DLC lccopycat QA76.9.D343 N54 2022 006.310151 23 0 Nield, Thomas (Computer programmer), author. Essential math for data science [electronic resource]: take control of your data with fundamental linear algebra, probability, and statistics / Thomas Nield. First edition. Beijing ; Boston : O'Reilly, 2022. 1 digital resource (xiv, 332 pages) ; 24 cm text txt rdacontent still image sti rdacontent unmediated n rdamedia volume nc rdacarrier Includes index. July 5, 2022 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. 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 -- Provided by publisher. Data mining Mathematics. Machine learning Mathematics. Mathematical statistics. Probabilities. Computer science Mathematics. Computer science Mathematics. fast (OCoLC)fst00872460 Data mining Mathematics. fast (OCoLC)fst02013374 Mathematical statistics. fast (OCoLC)fst01012127 Probabilities. fast (OCoLC)fst01077737 Handbooks and manuals. fast (OCoLC)fst01423877 Handbooks and manuals. lcgft CamTech Library 7 cbc copycat 2 ncip 20 y-gencatlg ddc BK 0 2402 2402 0 0 ddc 0 0 CamTech CamTech DC 2025-08-28 0 2025-08-28 14:31:40 1 digital 2025-08-28 BK