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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

By : Galea
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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

4 (5)
By: Galea

Overview of this book

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
Table of Contents (5 chapters)
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Chapter 3. Web Scraping and Interactive Visualizations

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...

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