Product details:
ISBN13: | 9783030709006 |
ISBN10: | 30307090011 |
Binding: | Hardback |
No. of pages: | 527 pages |
Size: | 279x210 mm |
Weight: | 1554 g |
Language: | English |
Illustrations: | 18 Illustrations, black & white; 130 Illustrations, color |
496 |
Category:
Probability and mathematical statistics
Mathematical Theory of computing
Additional devices
Further readings in mathematics
Probability and mathematical statistics (charity campaign)
Mathematical Theory of computing (charity campaign)
Additional devices (charity campaign)
Further readings in mathematics (charity campaign)
Statistics with Julia
Fundamentals for Data Science, Machine Learning and Artificial Intelligence
Series:
Springer Series in the Data Sciences;
Edition number: 1st ed. 2021
Publisher: Springer
Date of Publication: 4 September 2021
Number of Volumes: 1 pieces, Book
Normal price:
Publisher's listprice:
EUR 192.59
EUR 192.59
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63 577 (60 550 HUF + 5% VAT )
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Discount is valid until: 30 June 2024
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Short description:
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.
Long description:
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.
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.
Table of Contents:
Introducing 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.- Appendix A: How-to in Julia.- Appendix B: Additional Julia Features.- Appendix C: Additional Packages.