Overview
- Includes over 200 short code examples to illustrate dozens of key statistics concepts
- Solidifies the understanding of probability and statistics of professionals that are already working in data science, machine learning, or artificial intelligence
- Focuses on concepts to improve fundamental understanding
Part of the book series: Springer Series in the Data Sciences (SSDS)
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Table of contents (10 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Statistics with Julia
Book Subtitle: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
Authors: Yoni Nazarathy, Hayden Klok
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-030-70901-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-70900-6Published: 04 September 2021
Softcover ISBN: 978-3-030-70903-7Published: 05 September 2022
eBook ISBN: 978-3-030-70901-3Published: 04 September 2021
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XII, 527
Number of Illustrations: 18 b/w illustrations, 130 illustrations in colour
Topics: Mathematical Software, Statistics for Business, Management, Economics, Finance, Insurance, Data Structures, Probability and Statistics in Computer Science