# 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](https://statisticswithjulia.org/index.html), or go directly to Springer:

[![bookCoverImage](https://statisticswithjulia.org/img/book1Cover.png)](https://www.springer.com/gp/book/9783030709006)

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

1. Introducing Julia
2. Basic Probability
3. Probability Distributions
4. Processing and Summarizing Data
5. Statistical Inference Concepts
6. Confidence Intervals
7. Hypothesis Testing
8. Linear Regression and Extensions
9. Machine Learning Basics
10. Simulation of Dynamic Models

<ol type="A">
	<li> How-to in Julia</li>
	<li>Additional Language Features</li>
	<li>Additional Packages</li>
</ol>

## Usage instructions:

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

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An alternative is to use Pluto. See [StatisticsWithJuliaPlutoNotebooks.jl](https://github.com/StatisticalRethinkingJulia/StatisticsWithJuliaPlutoNotebooks.jl)

We hope you find this an enjoyable and instructive resource.

H.Klok
Y.Nazarathy