Chapter 1, Installing and Setting Up Julia, introduces the use of the Julia command line and the setup of the entire Julia computational infrastructure, including building Julia, optimizing performance, and configuring Julia for the cloud.Â
Chapter 2, Data Structures and Algorithms, contains practical examples of how custom algorithms can be implemented, while also taking advantage of the built-in functionality.
Chapter 3, Data Engineering in Julia, explains that working with data requires good understanding of streams and data sources. In this chapter, the reader will learn how to write data to IO streams with Julia and how to handle web transfers.
Chapter 4, Numerical Computing with Julia, contains recipes showing how computing tasks can be performed in the Julia language. Each recipe implements a relatively simple and standard algorithm to show a specific feature of the language. Therefore, the reader can concentrate on the implementation issues.
Chapter 5, Variables, Types, and Functions, presents topics related to variables and their scoping, Julia type systems and processing functions, and exceptions in Julia.
Chapter 6, Metaprogramming and Advanced Typing, presents various advanced programming topics in Julia.
Chapter 7, Handling Analytical Data, presents the DataFrames.jl package, providing a rich set of functionalities for working with them—manipulating rows and columns, handling categorical and missing data, and various standard transformations of tables (filtering, sorting, joins, wide-long transformation, and tabulation).
Chapter 8, Julia Workflow, explains the recommended workflow and shows how to build it using modules.
Chapter 9, Data Science, explains that Julia provides great support for various numerical and data science tasks. It allows us to define and optimize models in a very flexible solver-agnostic way. Julia also contains a huge toolbox for visualizing data and machine learning.Â
Chapter 10, Distributed Computing, shows how to use Julia for parallel and distributed computing tasks. An important feature of Julia is the ability to scale up computations across many processes, threads, and up to distributed computational clusters.
