So far in this book, we have focused on using Jupyter to build reproducible data analysis pipelines and predictive models. We'll continue to explore these topics in this lesson, but the main focus here is data acquisition. In particular, we will show you how data can be acquired from the web using HTTP requests. This will involve scraping web pages by requesting and parsing HTML. We will then wrap up this lesson by using interactive visualization techniques to explore the data we've collected.
The amount of data available online is huge and relatively easy to acquire. It's also continuously growing and becoming increasingly important. Part of this continual growth is the result of an ongoing global shift from newspapers, magazines, and TV to online content. With customized newsfeeds available all the time on cell phones, and live-news sources such as Facebook, Reddit, Twitter, and YouTube, it's difficult to imagine the historical alternatives being relevant much longer. Amazingly, this accounts for only some of the increasingly massive amounts of data available online.
With this global shift toward consuming content using HTTP services (blogs, news sites, Netflix, and so on), there are plenty of opportunities to use data-driven analytics. For example, Netflix looks at the movies a user watches and predicts what they will like. This prediction is used to determine the suggested movies that appear. In this lesson, however, we won't be looking at "business-facing" data as such, but instead we will see how the client can leverage the internet as a database. Never before has this amount and variety of data been so easily accessible. We'll use web-scraping techniques to collect data, and then we'll explore it with interactive visualizations in Jupyter.
Interactive visualization is a visual form of data representation, which helps users understand the data using graphs or charts. Interactive visualization helps a developer or analyst present data in a simple form, which can be understood by non-technical personnel too.