In this first part of the book, you will learn key Julia skills that are useful in data science projects. I have organized the content to gradually become more advanced. We start with the basic syntax of Julia and finish with advanced topics like parsing JSON data and working with missing values.
- Chapter 2 discusses the basics of Julia’s syntax and common language constructs, as well as the most important aspects of variable scoping rules.
- Chapter 3 introduces Julia’s type system and defining methods. It also introduces working with packages and modules. Finally, it discusses using macros.
- Chapter 4 covers working with arrays, dictionaries, tuples, and named tuples.
- Chapter 5 discusses advanced topics related to working with collections in Julia: broadcasting and subtyping rules for parametric types. It also covers integrating Julia with Python, using an example of a t-SNE dimensionality reduction algorithm.
- Chapter 6 teaches you various aspects of working with strings in Julia. Additionally, it covers the topics of using symbols, fixed-width strings, and compressing vectors by using the PooledArrays.jl package.
- Chapter 7 concentrates on working with time-series data and missing values. It also covers fetching data by using HTTP queries and parsing JSON data.