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Julia is a new open source programming language that is used in the field of data science computing. It was created to solve the dilemma between high-level slow code and fast but low-level code, and the necessity to use both to achieve high performance. This book will give you a head start to tackle your numerical and data problems with Julia. Your journey will begin by learning how to set up a running Julia platform before exploring its various built-in types. You will then move on to cover the different functions and constructs in Julia. The book will then walk you through the two important collection types―arrays and matrices. Over the course of the book, you will also be introduced to homoiconicity, the meta-programming concept in Julia.
The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science.
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.
Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.
requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code. Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia.
In this learning path, you will learn to use an interesting and dynamic programming language—Julia! You will get a chance to tackle your numerical and data problems with Julia. You'll begin the journey by setting up a running Julia platform before exploring its various built-in types. We'll then move on to the various functions and constructs in Julia. We'll walk through the two important collection types—arrays and matrices in Julia. By the end of this learning path, you will see how data works using simple statistics and analytics, and you'll discover its high and dynamic performance—its real strength, which makes it particularly useful in highly intensive computing tasks.