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

This repository is a collection of all 200+ code blocks contained in the book. See the Book's website, or go directly to Springer:

The book is comprised of the following ten chapters and three appendices:

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

How-to in Julia
Additional Language Features
Additional Packages

Usage instructions:

Clone or download this repository or a fork of it.
Have Julia 1.4 or above installed.
Run init.jl to install and precompile the required packages.
Run individual code examples.

An alternative is to use Pluto. See StatisticsWithJuliaPlutoNotebooks.jl

We hope you find this an enjoyable and instructive resource.

H.Klok Y.Nazarathy