Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj 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 (12 chapters)

Introduction


This chapter deals with the importance of the Julia programming language for data science and its applications. It also serves as a guide to handling data in the most available formats and also shows how to crawl and scrape data from the Internet.

Data Science pipelines that are used for production purposes need to be robust and highly fault-tolerant, without which the teams would be exposed highly error-prone models. So, these pipelines contain a subprocess called Extract-Transform-Load (ETL), in which the Extraction step involves pulling the data from a source, the Transform step involves the transforms performed on the dataset as part of the cleansing process, and the Load step is about loading the now clean data into the local databases for use in production. This will chapter will also teach you how to interact with websites by sending and receiving data through HTTP requests. This would be the first step in any data science and analytics pipeline. So, this chapter will cover some of those methods through which data can be ingested into the pipeline through various data sources.