Network Science with Python : Explore the Networks Around Us Using Network Science, Social Network Analysis, and Machine Learning

Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color Key Features 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 Purchase of the print or Kindle book includes a free PDF eBook 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. Table of Contents Introducing Natural Language Processing Network Analysis Useful Python Libraries NLP and Network Synergy Even Easier Scraping Graph Construction and Cleaning Whole Network Analysis Egocentric Network Analysis Community Detection Supervised Machine Learning on Network Data Unsupervised Machine Learning on Network Data Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color Key Features* 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 * Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNetwork 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 forNetwork 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. Table of Contents1. Introducing Natural Language Processing 2. Network Analysis 3. Useful Python Libraries 4. NLP and Network Synergy 5. Even Easier Scraping 6. Graph Construction and Cleaning 7. Whole Network Analysis 8. Egocentric Network Analysis 9. Community Detection 10. Supervised Machine Learning on Network Data 11. Unsupervised Machine Learning on Network Data Get hands-on approach to Network Science with implementation and examples that will have you up-and-running, and productive in no time.Key Features* Understand the commercial scene for network visualization software* Learn about Networks and Unsupervised Machine Learning* Get to grips with Networks and Supervised Machine LearningBook DescriptionThis book will be explaining topics from social science and mathematics in a way that is hands on and practical. It will be taught in a way so that readers will be inspired to use it to understand complex relationships that exist around them in their work and personal lives. Data Scientists, Software Engineers, and NLP Engineers will be able to put their knowledge to work with this practical guide to Network Analysis. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by learning the basics of NLP, Network Science, and Social Network Analysis and then will learn to programmatically build and analyze networks in order to understand the world around you. We will learn the science, where data comes from, how to get it, how to interact with it, and how to pull insights from it. However, this will be a hands-on book, not a math book, but you will be provided sources to look to for more specific technical and mathematical details.By the end of the book you will be able to identify network data and use it to extract unconventional insights to make sense of the complex world that exists around you.What you will learn* Get familiar with NLP, Network Science, and Social Network Analysis.* Get familiar with the tech stack used to apply NLP, Network Science, and Social Network Analysis.* Learn how to get and prepare NLP and network data* Learn how to extract insights from NLP and network data* Understand how to think up your own NLP and network projects* Understand how to authenticate and scrape tweets, connection, and the streamWho This Book Is ForData Scientists, NLP Engineers, Software Engineers, Social Scientists, and Students with introductory software skills, some Data Science, some statistics skills will find this book useful. Having python programming intermediate skills is necessary to get the best from this book Network Science with Python provides a hands-on approach to the theory and implementation of network science. You'll get a comprehensive explanation of how to use graph networks for data science using Python.