Book Image

Learning Julia

By : Anshul Joshi, Rahul Lakhanpal
Book Image

Learning Julia

By: Anshul Joshi, Rahul Lakhanpal

Overview of this book

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback
Data Visualization and Graphics

Chapter 1. Understanding Julia's Ecosystem

Julia is a new programming language compared to other existing popular programming languages. Julia was presented publicly to the world and became open source in February of 2012. It all started in 2009, when three developers—Viral Shah, Stefan Karpinski, and Jeff Bezanson at the Massachusetts Institute of Technology (MIT), under the supervision of Professor Alan Edelman in the Applied Computing group—started working on a project. This lead to Julia. All of the principal developers are still actively involved with the JuliaLang. They are committed not just to the core language but to the different libraries that have evolved in its ecosystem. Julia is based on solid principles, which we will learn throughout the book. It is becoming more famous day by day, continuously gaining in the ranks of the TIOBE index (currently at 43), and gaining traction on Stack Overflow. Researchers are attracted to it, especially those from a scientific computing background.

Anyone can check the source code, which is available on GitHub ( The current release at the time of writing this book is 0.6 with 633 contributors, 39,010 commits, and 9,398 stars on GitHub. Most of the core is written in Julia itself and there are a few chunks of code in C/C++, Lisp, and Scheme.

This chapter will take you through the installation and a basic understanding of all the necessary components of Julia. This chapter covers the following topics:

  • What makes Julia unique?
  • Installing Julia
  • Julia's importance in data science
  • Using REPL
  • Using Jupyter Notebook
  • What is Juno?
  • Package management
  • A brief about multiple dispatch
  • Understanding LLVM and JIT