This is the code repository for Network Science with Python, published by Packt.
Explore the networks around us using network science, social network analysis, and machine learning
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.
This book covers the following exciting features:
- Explore NLP, network science, and social network analysis
- Apply the tech stack used for NLP, network science, and analysis
- Extract insights from NLP and network data
- Generate personalized NLP and network projects
- Authenticate and scrape tweets, connections, the web, and data streams
- Discover the use of network data in machine learning projects
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
morph_network_df = get_network_data(morph_entities)
morph_network_df.head()
Following is what you need for this book:
Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Python 3.6 and Jupyter Notebook | Any OS |
David Knickerbocker is the chief engineer and co-founder of VAST OSINT. He has over 2 decades of rich experience working with and around data in his career, focusing on data science, data engineering, software development, and cybersecurity. During his free time, David loves to use data science for creative endeavors. He also enjoys hanging out with his family and cats. He loves to read data science books outside, in the sun.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.