Book Image

Julia Cookbook

By : Raj R Jalem, Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how 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 book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (7 chapters)

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.