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Applied Data Science with Python and Jupyter

Applied Data Science with Python and Jupyter

By : Alex Galea
4.3 (3)
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Applied Data Science with Python and Jupyter

Applied Data Science with Python and Jupyter

4.3 (3)
By: Alex Galea

Overview of this book

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
Table of Contents (5 chapters)
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Interactive Visualizations


Visualizations are quite useful as a means of extracting information from a dataset. For example, with a bar graph it's very easy to distinguish the value distribution, compared to looking at the values in a table. Of course, as we have seen earlier in this book, they can be used to study patterns in the dataset that would otherwise be quite difficult to identify. Furthermore, they can be used to help explain a dataset to an unfamiliar party. If included in a blog post, for example, they can boost reader interest levels and be used to break up blocks of text.

When thinking about interactive visualizations, the benefits are similar to static visualizations, but enhanced because they allow for active exploration on the viewer's part. Not only do they allow the viewer to answer questions they may have about the data, they also think of new questions while exploring. This can benefit a separate party such as a blog reader or co-worker, but also a creator, as it allows...

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