Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

Creating Our First Julia App

Now that you have a working Julia installation and your IDE of choice is ready to run, it's time to put them to some good use. In this chapter, you'll learn how to apply Julia for data analysis—a domain that is central to the language, so expect to be impressed!

We will learn to perform exploratory data analysis with Julia. In the process, we'll take a look at RDatasets, a package that provides access to over 700 learning datasets. We'll load one of them, the Iris flowers dataset, and we'll manipulate it using standard data analysis functions. Then we'll look more closely at the data by employing common visualization techniques. And finally, we'll see how to persist and (re)load our data.

But, in order to do that, first we need to revisit and take a look at some of the language's most important building...