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Create networks using data points and information
Learn to visualize and analyze networks to better understand communities
Explore the use of network data in both - supervised and unsupervised machine learning projects
Book Description
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.
What you will learn
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
Who this book is for
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.
Learn about using graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide to using Python, printed in color.
Key features
Create networks using data points and information
Learn to visualize and analyze networks for better understanding communities
Explore the use of network data in both supervised and unsupervised machine learning projects
Book Description
Network analysis is often taught using tiny or toy data sets, leaving you with limited learning and practice. Network Science with Python helps you extract relevant data, draw conclusions, and build networks using industry-standard actionable datasets. You'll start by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically build and analyze networks. You will gain a practical understanding of the data source, retrieving data, interacting with it, and extracting information from it. This is a practical book with theoretical background, specific technical and mathematical details for further reference. As you progress, you will learn how to create and clean networks, perform network analysis, egocentric network analysis, community detection, and leverage network data using machine learning. You will also learn network analysis concepts from basic to advanced levels.
By the end of the book, you will be able to identify network data and use it to extract unconventional insights for understanding the complex world around you.
What you'll learn
Learn NLP, network science, and social network analysis
Apply the technology stack used for NLP, network science, and analysis
Extract insights from NLP and network data
Create personalized NLP and network designs
Authenticate and sanitize tweets, connections, the Internet, and data streams
What you'll learn about using network data in machine learning projects
Who is this book for
Network science using Python demonstrates how programming and social science can be combined to discover new ideas. Data scientists, NLP engineers, programmers, social scientists, and data science students will find this book useful. Intermediate level of Python programming is required. Readers from both social science and programming backgrounds will gain a new perspective and add a feather to their cap.