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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

Data analysis and processing

There are a number of useful libraries for working with data, and you will want to use different libraries and techniques at different points of the data life cycle. For instance, in working with data, it is often useful to start with Exploratory Data Analysis (EDA). Later on, you will want to do cleanup, wrangling, various transformations for preprocessing, and so on. Here are some of the available Python libraries and their uses.

pandas

pandas is easily one of the most important libraries to use when doing anything with data in Python. Put simply, if you work with data in Python, you should know about pandas, and you should probably be using it. You can use it for several different things when working with data, such as the following:

  • Reading data from an assortment of file types or from the internet
  • EDA
  • Extract, Transform, Load (ETL)
  • Simple and quick data visualizations
  • And much, much, more

If there is one Python...

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Network Science with Python
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