# What this book covers

Chapter 1, *Extracting and Handling Data*, deals with the importance of the Julia programming language for data science and its applications. It also serves as a guide to handle data in the most available formats, and shows how to crawl and scrape data from the Internet.

Chapter 2, *Metaprogramming*, covers the concept of metaprogramming, where 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, Julia can express its code as data structures.

Chapter 3, *Statistics with Julia*, teaches you how to perform statistics in Julia, along with the common problems of handling data arrays, distributions, estimation, and sampling techniques.

Chapter 4, *Building Data Science Models*, talks about various data science and statistical models. You will learn to design, customize, and apply them to various data science problems. This chapter will also teach you about model selection and the ways to learn how to build and understand robust statistical models.

Chapter 5, *Working with Visualizations*, teaches you how to visualize and present data, and also to analyze and the findings from the data science approach that you have taken to solve a particular problem. There are various types of visualizations to display your findings, namely the bar plot, the scatter plot, pie chart, and so on. It is very important to choose an appropriate method that can reflect your findings and work in a sensible and an aesthetically pleasing manner.

Chapter 6, *Parallel Computing*, talks about the concepts of parallel computing and handling a lot of data in Julia.