LEADER 04449nam 2200661 450 001 996466408803316 005 20230926180938.0 010 $a9783030709013 010 $a3-030-70901-9 035 $a(CKB)5590000000552008 035 $a(MiAaPQ)EBC6719958 035 $a(Au-PeEL)EBL6719958 035 $a(OCoLC)1287135412 035 $a(PPN)258054646 035 $a(EXLCZ)995590000000552008 100 $a20220610d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistics with Julia $efundamentals for data science, machine learning and artificial intelligence /$fYoni Nazarathy, Hayden Klok 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dŠ2021 215 $a1 online resource (531 pages) 225 1 $aSpringer Series in the Data Sciences 311 1 $a9783030709006 311 1 $a3-030-70900-0 327 $aIntroducing Julia -- Basic Probability -- Probability Distributions -- Processing and Summarizing Data -- Statistical Inference Concepts -- Confidence Intervals -- Hypothesis Testing -- Linear Regression and Extensions -- Machine Learning Basics -- Simulation of Dynamic Models 330 $a"This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book's associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With "Statistics with Julia", Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia." -- Publisher's description. 410 0$aSpringer series in the data sciences. 606 $aProbabilities$xData processing 606 $aStatistics$xData processing 606 $aEstadística$2thub 606 $aEstructures de dades (Informātica)$2thub 606 $aEstadística matemātica$2thub 606 $aProbabilitats$xInformātica$2lemac 606 $aEstadística$xInformātica$2lemac 606 $aAprenentatge automātic$2lemac 608 $aLlibres electrōnics$2thub 615 0$aProbabilities$xData processing. 615 0$aStatistics$xData processing. 615 7$aEstadística 615 7$aEstructures de dades (Informātica) 615 7$aEstadística matemātica 615 7$aProbabilitats$xInformātica 615 7$aEstadística$xInformātica 615 7$aAprenentatge automātic 676 $a519.2 700 $aNazarathy$b Yoni$01069553 702 $aKlok$b Hayden 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466408803316 996 $aStatistics with Julia$92874751 997 $aUNISA