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  • Book Overview & Buying Network Science with Python
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Network Science with Python

Network Science with Python

By : David Knickerbocker
5 (15)
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Network Science with Python

Network Science with Python

5 (15)
By: David Knickerbocker

Overview of this book

Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You’ll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You’ll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you’ll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You’ll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you’ll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.
Table of Contents (17 chapters)
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1
Part 1: Getting Started with Natural Language Processing and Networks
5
Part 2: Graph Construction and Cleanup
9
Part 3: Network Science and Social Network Analysis

Useful Python Libraries

In this chapter, I’m going to introduce several of the Python libraries we will be using in this book. I will describe what they are and how to install them, and give examples of a few useful things you can do with them. You do not have to memorize all of the capabilities of every Python library that you use, or thoroughly understand the internals of every single function that you use. It is important that you understand what libraries are available, what overall capabilities they have, and what critically important timesavers are in the library (this will keep you coming back for more). Everyone’s use case is different, and there is no way to memorize everything. I recommend that you internalize this point as quickly as possible, and learn what you need to when you need it. Learn about the internals in as much depth as you need.

To keep this organized, I am separating software libraries by category. Here are the libraries we will discuss:

...
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83
Tech Concepts
36
Programming languages
73
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Network Science with Python
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