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    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)
    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)
    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)
    Ebook315 pages2 hours

    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)

    By Timothy Eastridge

    ()

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    About this ebook

    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.

    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.

    Table of Contents
    1. Introduction to Graph Data Science
    2. Getting Started with Python and Neo4j
    3. Import Data into the Neo4j Graph Database
    4. Cypher Query Language
    5. Visualizing Graph Networks
    6. Enriching Neo4j Data with ChatGPT
    7. Neo4j Vector Index and Retrieval-Augmented Generation (RAG)
    8. Graph Algorithms in Neo4j
    9. Recommendation Engines Using Embeddings
    10. Fraud Detection
          CLOSING SUMMARY
            The Future of Graph Data Science
          Index
    LanguageEnglish
    PublisherOrange Education Pvt Ltd
    Release dateMar 11, 2024
    ISBN9788197081965
    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)

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      Book preview

      Graph Data Science with Python and Neo4j - Timothy Eastridge

      CHAPTER 1

      Introduction to Graph Data Science

      Introduction

      In this chapter, we will provide an introduction and overview of graph data science as a method to explore contextual relationships in data. We will explore the significance and versatility of graphs in various domains. Our daily lives are full of graphs, from social media to the maps we use to drive to work, to the recommendations provided to us on our favorite TV streaming network.

      We will analyze Python and Neo4j as the tools to learn and explore graphs. These tools offer extensive libraries as well as robust community support, which makes them a great choice for the journey of graph data science.

      Structure

      In this chapter, the following topics will be covered:

      Understanding Graphs, Graph Networks, and their Relevance

      Introduction to Neo4j Graph Database

      Overview of the Importance of Graph Visualizations

      Data Science and Machine Learning

      Introduction to Graph Data Science

      Introduction to the Python Programming Language

      Data Science and Machine Learning

      Before we jump into the fascinating world of graph data science, it’s important to clarify two fundamental terms: data science and machine learning. While we assume a certain level of familiarity with these concepts, we will guide you along the way.

      Data Science is a multidisciplinary field that involves extracting knowledge and insights from data through various techniques such as data mining, data visualization, and statistical analysis. Data science involves the end-to-end process of acquiring, cleaning, transforming, and analyzing data to uncover patterns, make predictions, and drive better decision-making:

      Figure 1.1: Visual of end-to-end data analysis (Source: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRXeY_2rpyHpnH9QJYk61usUIQ1NTXrWrQefA&usqp=CAU)

      Machine Learning, on the other hand, is a subset of data science that focuses on developing algorithms and models that enable computers to learn from data and make predictions or take actions without being explicitly programmed. Machine learning algorithms learn from historical data to identify patterns, make predictions, and automate decision-making processes on new, never-before-seen data.

      Figure 1.2: Visual of a computer processing a large amount of historical data and then exporting predictions (Source: https://www.dataversity.net/future-analytics-hype-real/)

      In summary, while the two are often used as synonyms, data science provides the foundation and tools to explore, interpret, and gain insights from data, while machine learning leverages the data to build predictive models and make accurate predictions and/or automated decisions. Together, the two form a powerful combination that drives innovation and enables data-driven solutions.

      Defining Graph

      While you might first think of a graph as a pie chart or an x and y axis, we refer to a graph in this book as something else entirely. In discrete mathematics and graph theory, a graph is a structure that consists of objects or nodes (illustrated as dots in Figure 1.3) where pairs of objects or nodes are connected or related in some way. These objects can be referred to as vertices, nodes, or points. In this book, we will refer to these objects as nodes.

      The connections between the vertices are referred to as edges, relationships, or links (illustrated as lines connecting the dots in Figure 1.3). In this book, we will refer to the connections between nodes as relationships.

      In Neo4j, data can be stored on both nodes and relationships. We will refer to this data as properties of either the node or relationship:

      Figure 1.3: Nodes and Relationships (created using

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