Statistics with Julia : Fundamentals for Data Science Machine Learning and Artificial Intelligence 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. 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.