Book Image

Beginning Data Science with Python and Jupyter

By : Alex Galea
Book Image

Beginning Data Science with Python and Jupyter

By: Alex 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 (7 chapters)

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...