A hands-on guide for data science practitioners which uses a code-based approach to introduce the main features of Julia.

• Augment your basic computing skills with an in-depth introduction to Julia
• Focus on topic-based approaches to scientific problems and visualisation
• Build on prior knowledge of programming languages such as Python, R, C/C++

Julia is a well-constructed programming language which has matured over the last 11 years since the introduction in 2012 and has now reached a degree of stability in its coding and data structures. It 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.

In addition there has been a rapid growth in the number of Julia packages available to tackle individual subject areas such as data handling, statistical analysis and visualization, as well as considerable growth in the areas of machine learning and Bayesian statistics.

This book starts off with a refresher on installing and running Julia on different platforms and a discussion on using Julia on various code editors and interactive development environments (IDEs).

You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia.

• Develop simple scripts in Julia using the REPL, code editors and web-based IDEs
• Introduce the features of the 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 A.I

This book is not intended as an introduction to computer programming. Rather it is aimed at those who wish to enhance their basic knowledge of Julia and/or those using a different programming language who wish to augment their skillset by adding Julia to it. Also it will be of interest as a primer on Julia’s approach to a wide variety of topics such as: scientific computing, statistics, machine learning, simulation, graphics, distributed computing.

1. The Julia Environment
2. Developing in Julia
3. Types and Dispatch
4. The Three Ms
5. Interoperability
6. Working with Data
7. Scientific Programming
8. Graphics
9. Databases
10. Machine Learning
11. Working with Julia
12. Julia’s Backpages