Julia is a high-performance dynamic programming language for numerical computing. This practical guide to programming with Julia will help you to work with data more efficiently.

This course begins with the important features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll explore utilizing the Julia language to identify, retrieve, and transform datasets so you can perform efficient data analysis and data manipulation.

You will then learn the concepts of metaprogramming and statistics in Julia.

Moving on, you will learn to build data science models by using several algorithms such as dimensionality reduction, linear discriminant analysis, and so on.

You’ll learn 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 course includes sections on identifying and classifying data science problems, data modelling, data analysis, data manipulation, multidimensional arrays, and parallel computing.

By the end of this course, you will acquire the skills to work more effectively with your data.

Style and Approach

This carefully curated course follows a video-based approach to help you grasp the concepts of Julia programming.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

• Julia Cookbook by Jalem Raj Rohit
• Julia Solutions by Jalem Raj Rohit

Note: This interactive EPUB adheres to the latest specification, and requires that your reader supports video and interactive content. We recommend using Readium with the latest stable version of Google Chrome, or iBooks for OSX.