Book Image

Julia Cookbook

By : Raj R Jalem, Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (7 chapters)

Introduction


Metaprogramming is a concept where by a language can express its own code as a data structure of itself. For example, Lisp expresses code in the form of Lisp arrays, which are data structures in Lisp itself. Similarly, even Julia can express its code as data structures.

This makes it possible for Julia to generate and transform code through a Julia program. Julia has really nice reflection properties. So, the property of metaprogramming makes it easy to handle repetitive programming and function execution in data science and, especially, while handling big data in the Map Reduce framework.