Mastering Julia - Tackle the Contemporary Challenges of Programming and Data Science with Julia

by Malcolm Sherrington

Key Features Build statistical models with linear regression and analysis of variance (ANOVA) Create your own modules and contribute to the Julia package system Complete an extensive data science project through the entire cycle from ETL to analytics and data visualization Book Description

Julia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second. This book will help you develop and enhance your programming skills in Julia to solve real-world automation challenges.

This book starts off with a refresher on installing and running Julia on different platforms. Next, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build. You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia.

What You Will Learn Install and build Julia and configure it with your environment Build a data science project through the entire cycle of ETL, analytics, and data visualization Understand the type system and principles of multiple dispatch for a better coding experience in Julia Interact with data files and data frames to study simple statistics and analytics Display graphics and visualizations to carry out modeling and simulation in Julia Use Julia to interact with SQL and NoSQL databases Work with distributed systems on the Web and in the cloud Develop your own packages and contribute to the Julia Community About the Author

Malcolm Sherrington has been working in computing for over 35 years. He currently runs his own company in the finance sector, and is particularly interested in High Performance Computing and applications of GPUs and parallelism. Malcolm is also the organizer of the London Julia User Group, and co-organizer of the UK High Performance Computing and the financial engineers and Quant London meetup groups.

Table of Contents The Julia Environment Developing in Julia Types and Dispatch Interoperability Working with Data Scientific Programming Graphics Databases Networking Working with Julia

Genres: Programming

412 pages, Kindle Edition

First published July 22, 2015

Reviews

[Max Lybbert · Rating 3 out of 5]

Mastering Julia is a relatively quick read. Sherrington covers a lot of material in a very organized and logical manner, does a great job of teaching the language, and gives an extensive overview of available modules. Sherrington gives some software development advice. It's clear he has significant experience.

Unfortunately, a few of the examples are a little too contrived. The introduction to shelling out commands is especially bad, although I will grant Sherrington that it's hard to find a task that can't already be solved with a Julia module. But I wish he didn't suggest that you might want exec "ls" to list the files in a folder instead of using a regular function call. This chapter should have had a large disclaimer: "The overhead of forking a separate process is incredibly high. The examples in this chapter show how to interact with the operating system and other processes, but they don't necessarily show practical ways to accomplish many of the tasks."

I read this as an ebook. I've noticed that other ebooks have surprising quality problems, and John Walker's book reviews often point out problems with ebooks that don't appear in the print edition. This ebook had a large number of problems you'd expect a copyeditor to find, but I have no idea if they also appear in print. In some cases, sentences are obviously frozen mid-edit (e.g., "generally you'll want to generally ..."); in other cases soundalike words appear (e.g., "in" instead of "an"); and there are very awkwardly worded sentences. There's an example of getting data from a web service, in this getting case Apple's stock price from Quandl. The text incorrectly says that Apple's ticker is "APPL," while the example code correctly says it's "APPL."

Some of the mistakes are funny: I smiled every time I read that some function had been "depreciated" instead of "deprecated." While these kinds of problems aren't unusual with ebooks, there were enough of them that they became somewhat distracting, similar to the first few chapters of Half Luck and Half Brains: The Kemmons Wilson Holiday Inn Story and an early copy of The Four Steps to the Epiphany: Successful Strategies for Startups That Win.