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.