Название: 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 StrategiesАвтор: Timothy Eastridge
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 204
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB
Practical approaches to leveraging graph Data Science to solve real-world challenges.
Key Features:
- Explore the fundamentals of graph Data Science, its importance, and applications.
- Learn how to set up Python and Neo4j environments for graph data analysis.
- Discover techniques to visualize complex graph networks for better understanding.
Book Description:
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.
Python and Neo4j are both terrific tools for graph data science. Each offers powerful tools and frameworks to analyze interconnected data. In theory, it is possible to implement end-to-end graph data science with either Python or Neo4j individually. However, in practice, it is much simpler to leverage both tools together to maximize the value of each tool.
Python has become one of the most popular programming languages in the data science community due to its rich ecosystem of libraries and packages, allowing all users to leverage the incredible work of others and jumpstart their analysis. Python comes with several integrated development environments (IDEs) that facilitate code visualization, allowing users not only to view and debug their code but also to create graphs, charts, and various other visual representations of data.
Neo4j is a highly efficient native graph database well-known for its ability to manage complex data relationships effectively. It is the most popular and prominent graph database, which means the community and documentation are mature and well-established. The vibrant community of developers sets Neo4j apart. Being an open-source platform, it benefits from contributions from these developers. This consolidation of a strong community and efficient speed makes Neo4j a great choice in the realm of graph databases.
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.
What you will learn:
- Import and manipulate data within the Neo4j graph database using Cypher Query Language.
- Visualize complex graph networks to gain insights into data relationships and patterns.
- Enhance data analysis by integrating ChatGPT for context-rich data enrichment.
- Explore advanced topics including Neo4j vector indexing and Retrieval-Augmented Generation (RAG).
- Develop recommendation engines leveraging graph embeddings for personalized suggestions.
- Build and deploy recommendation systems and fraud detection models using graph techniques.
- Gain insights into the future trends and advancements shaping the field of graph data science.
Who is this book for?
This book caters to a diverse audience interested in leveraging the power of graph data science using Python and Neo4j. It includes Data Science Professionals, Software Engineers, Academic Researchers, Business Analysts, and Technology Hobbyists. This comprehensive book equips readers from various backgrounds to effectively utilize graph data science in their respective fields.
Contents:
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