Graph Data Science with Python and Neo4j : Hands-On Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data Science for Building Enterprise Strategies (English Edition)

• Record Nr.: UNINA9910985653903321
• Autore: Eastridge Timothy
• Pubbl/distr/stampa: Delhi : , : Orange Education PVT Ltd, , 2024; ©2024
• ISBN: 9788197081965; 8197081964
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (118 pages)
• Soggetti: Graph databases; Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover Page -- Title Page -- Copyright Page -- Dedication Page -- About the Author -- About the Technical Reviewer -- Acknowledgements -- Preface -- A Note from the Author -- Errata -- Table of Contents -- 1. Introduction to Graph Data Science -- Introduction -- Structure -- Data Science and Machine Learning -- Defining Graph -- The importance of Graph Structures -- Introducing Neo4j Graph Database -- Knowledge Graphs -- Introducing Python Programming Language -- Conclusion -- Multiple Choice Questions -- Answers -- 2. Getting Started with Python and Neo4j -- Introduction -- Structure -- Installing and Setting Up Python and Neo4j -- Installing Python -- Executing Python Code Using Common Libraries -- Incorporating New Libraries Using Conda
• Sommario/riassunto: Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph data science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical ; application, providing you with the hands-on skills needed to tackle real-world challenges. You'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. This book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data.