Julia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second.
This book is a primer on Julia’s approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing.
Starting off with a refresher on installing and running Julia on different platforms, you’ll quickly get to grips with the core concepts and delve into a discussion on how to use Julia with various code editors and interactive development environments (IDEs).
As you progress, you’ll see how data works through simple statistics and analytics and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks. You’ll also and observe how Julia can cooperate with external processes to enhance graphics and data visualization. Finally, you will explore metaprogramming and learn how it adds great power to the language and establish networking and distributed computing with Julia.
By the end of this book, you’ll be confident in using Julia as part of your existing skill set.
Develop simple scripts in Julia using the REPL, code editors, and web-based IDEs
Get to grips with Julia’s type system, multiple dispatch, metaprogramming, and macro development
Interact with data files, tables, data frames, SQL, and NoSQL databases
Delve into statistical analytics, linear programming, and optimization problems
Create graphics and visualizations to enhance modeling and simulation in Julia
Understand Julia's main approaches to machine learning, Bayesian analysis, and AI