Graph Data Modeling in Python : A practical guide to curating, analyzing, and modeling data with graphs
Language: English
Published by Packt Publishing, 2023
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 70.71
Quantity: 1 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming languagePurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Transform relational data models into graph data model while learning key applications along the wayDiscover common challenges in graph modeling and analysis, and learn how to overcome themPractice real-world use cases of community detection, knowledge graph, and recommendation networkBook Description:Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements.By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time.What You Will Learn:Design graph data models and master schema design best practicesWork with the NetworkX and igraph frameworks in PythonStore, query, ingest, and refactor graph dataStore your graphs in memory with Neo4jBuild and work with projections and put them into practiceRefactor schemas and learn tactics for managing an evolved graph data modelWho this book is for:If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.…
Seller Inventory # 9781804618035
- Title
- Graph Data Modeling in Python : A practical guide to curating, analyzing, and modeling data with graphs
- Author
- Gary Hutson
- Publisher
- Packt Publishing
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1804618039
- ISBN 13
- 9781804618035
- Item weight
- 449 grams
- Dimensions
- 235x191x13 mm
Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language
Purchase of the print or Kindle book includes a free PDF eBook
Key Features:
- Transform relational data models into graph data model while learning key applications along the way
- Discover common challenges in graph modeling and analysis, and learn how to overcome them
- Practice real-world use cases of community detection, knowledge graph, and recommendation network
Book Description:
Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.
Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements.
By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time.
What You Will Learn:
- Design graph data models and master schema design best practices
- Work with the NetworkX and igraph frameworks in Python
- Store, query, ingest, and refactor graph data
- Store your graphs in memory with Neo4j
- Build and work with projections and put them into practice
- Refactor schemas and learn tactics for managing an evolved graph data model
Who this book is for:
If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.
"Synopsis" may belong to another edition of this title.
About the Author
Matt Jackson is a lead data scientist specializing in graph theory and network analytics. His interest in graphs was sparked during his PhD in systems biology, where network analysis was used to uncover novel features of cell organization. Since then, he has worked in diverse industries - from academia to intelligence, highlighting patterns and risk in complex data by harnessing the latest in graph algorithms and machine learning.
"About the title" may belong to another edition of this title.
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