Graph Data Modeling In Python

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Graph Data Modeling in Python

Author : Gary Hutson
Publisher : Packt Publishing Ltd
ISBN 13 : 1804619345
Total Pages : 236 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Graph Data Modeling in Python by : Gary Hutson

Download or read book Graph Data Modeling in Python written by Gary Hutson and published by Packt Publishing Ltd. This book was released on 2023-06-30 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python for Graph and Network Analysis

Author : Mohammed Zuhair Al-Taie
Publisher : Springer
ISBN 13 : 3319530046
Total Pages : 214 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Python for Graph and Network Analysis by : Mohammed Zuhair Al-Taie

Download or read book Python for Graph and Network Analysis written by Mohammed Zuhair Al-Taie and published by Springer. This book was released on 2017-03-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

Graph Algorithms

Author : Mark Needham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492047635
Total Pages : 297 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Graph Algorithms by : Mark Needham

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Handbook of Graphs and Networks in People Analytics

Author : Keith McNulty
Publisher : CRC Press
ISBN 13 : 100059727X
Total Pages : 266 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Handbook of Graphs and Networks in People Analytics by : Keith McNulty

Download or read book Handbook of Graphs and Networks in People Analytics written by Keith McNulty and published by CRC Press. This book was released on 2022-06-19 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form. The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level. Key features: Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.

Neo4j Graph Data Modeling

Author : Mahesh Lal
Publisher : Packt Publishing Ltd
ISBN 13 : 178439730X
Total Pages : 138 pages
Book Rating : 4.7/5 (843 download)

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Book Synopsis Neo4j Graph Data Modeling by : Mahesh Lal

Download or read book Neo4j Graph Data Modeling written by Mahesh Lal and published by Packt Publishing Ltd. This book was released on 2015-07-27 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neo4j is a graph database that allows you to model your data as a graph and find solutions to complex real-world problems that are difficult to solve using any other type of database. This book is designed to help you understand the intricacies of modeling a graph for any domain. The book starts with an example of a graph problem and then introduces you to modeling non-graph problems using Neo4j. Concepts such as the evolution of your database, chains, access control, and recommendations are addressed, along with examples and are modeled in a graph. Throughout the book, you will discover design choices and trade-offs, and understand how and when to use them. By the end of the book, you will be able to effectively use Neo4j to model your database for efficiency and flexibility.

Graph Data Science with Python and Neo4j

Author : Timothy Eastridge
Publisher : Orange Education Pvt Ltd
ISBN 13 : 8197081964
Total Pages : 226 pages
Book Rating : 4.1/5 (97 download)

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Book Synopsis Graph Data Science with Python and Neo4j by : Timothy Eastridge

Download or read book Graph Data Science with Python and Neo4j written by Timothy Eastridge and published by Orange Education Pvt Ltd. This book was released on 2024-03-11 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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 WILL YOU LEARN ● Set up and utilize Python and Neo4j environments effectively for graph analysis. ● 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

Hands-On Graph Analytics with Neo4j

Author : Estelle Scifo
Publisher : Packt Publishing Ltd
ISBN 13 : 1839215666
Total Pages : 496 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Hands-On Graph Analytics with Neo4j by : Estelle Scifo

Download or read book Hands-On Graph Analytics with Neo4j written by Estelle Scifo and published by Packt Publishing Ltd. This book was released on 2020-08-21 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

Learning Neo4j

Author : Rik Van Bruggen
Publisher : Packt Publishing Ltd
ISBN 13 : 1849517177
Total Pages : 296 pages
Book Rating : 4.8/5 (495 download)

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Book Synopsis Learning Neo4j by : Rik Van Bruggen

Download or read book Learning Neo4j written by Rik Van Bruggen and published by Packt Publishing Ltd. This book was released on 2014-08-25 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.

Graph Data Science with Neo4j

Author : Estelle Scifo
Publisher : Packt Publishing Ltd
ISBN 13 : 1804614904
Total Pages : 289 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Graph Data Science with Neo4j by : Estelle Scifo

Download or read book Graph Data Science with Neo4j written by Estelle Scifo and published by Packt Publishing Ltd. This book was released on 2023-01-31 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesExtract meaningful information from graph data with Neo4j's latest version 5Use Graph Algorithms into a regular Machine Learning pipeline in PythonLearn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.Book Description Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You'll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you'll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you'll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you'll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions. What you will learnUse the Cypher query language to query graph databases such as Neo4jBuild graph datasets from your own data and public knowledge graphsMake graph-specific predictions such as link predictionExplore the latest version of Neo4j to build a graph data science pipelineRun a scikit-learn prediction algorithm with graph dataTrain a predictive embedding algorithm in GDS and manage the model storeWho this book is for If you're a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you'll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.

Graph Machine Learning

Author : Claudio Stamile
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206755
Total Pages : 338 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Graph Machine Learning by : Claudio Stamile

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Graph Models for Deep Learning

Author : Stephen Donald Huff
Publisher : Executive Reviews
ISBN 13 : 9781723761263
Total Pages : 176 pages
Book Rating : 4.7/5 (612 download)

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Book Synopsis Graph Models for Deep Learning by : Stephen Donald Huff

Download or read book Graph Models for Deep Learning written by Stephen Donald Huff and published by Executive Reviews. This book was released on 2018-09-16 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This course provides a detailed executive-level review of contemporary topics in graph modeling theory with specific focus on Deep Learning theoretical concepts and practical applications. The ideal student is a technology professional with a basic working knowledge of statistical methods. Upon completion of this review, the student should acquire improved ability to discriminate, differentiate and conceptualize appropriate implementations of application-specific ('traditional' or 'rule-based') methods versus deep learning methods of statistical analyses and data modeling. Additionally, the student should acquire improved general understanding of graph models as deep learning concepts with specific focus on state-of-the-art awareness of deep learning applications within the fields of character recognition, natural language processing and computer vision. Optionally, the provided code base will inform the interested student regarding basic implementation of these models in Keras using Python (targeting TensorFlow, Theano or Microsoft Cognitive Toolkit).As an 'executive review', this text presents a distillation of essential information without the clutter of formulae, charts, graphs, references and footnotes. Thus, the student will not have a 'textbook' experience (or expense) while reviewing its contents. Instead, the student will quickly pass through a surprising wealth of actionable, easily-digestible technological information without the distraction of extemporaneous considerations.

Hands-On Big Data Modeling

Author : James Lee
Publisher : Packt Publishing Ltd
ISBN 13 : 1788626087
Total Pages : 293 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Hands-On Big Data Modeling by : James Lee

Download or read book Hands-On Big Data Modeling written by James Lee and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve all big data problems by learning how to create efficient data models Key FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learnGet insights into big data and discover various data modelsExplore conceptual, logical, and big data modelsUnderstand how to model data containing different file typesRun through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modelingCreate data models such as Graph Data and Vector SpaceModel structured and unstructured data using Python and RWho this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.

Graph Data-Models and Semantic Web Technologies in Scholarly Digital Editing

Author : Elena Spadini
Publisher : BoD – Books on Demand
ISBN 13 : 3754343696
Total Pages : 226 pages
Book Rating : 4.7/5 (543 download)

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Book Synopsis Graph Data-Models and Semantic Web Technologies in Scholarly Digital Editing by : Elena Spadini

Download or read book Graph Data-Models and Semantic Web Technologies in Scholarly Digital Editing written by Elena Spadini and published by BoD – Books on Demand. This book was released on 2021-10-06 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: In scholarly digital editing, the established practice for semantically enriching digital texts is to add markup to a linear string of characters. Graph data-models provide an alternative approach, which is increasingly being given serious consideration. Labelled-property-graph databases, and the W3c's semantic web recommendation and associated standards (RDF and OWL) are powerful and flexible solutions to many of the problems that come with embedded markup. This volume explores the combination of scholarly digital editions, the graph data-model, and the semantic web from three perspectives: infrastructures and technologies, formal models, and projects and editions.

Graph Algorithms for Data Science

Author : Tomaž Bratanic
Publisher : Simon and Schuster
ISBN 13 : 1617299464
Total Pages : 350 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Graph Algorithms for Data Science by : Tomaž Bratanic

Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

Python Data Science Handbook

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 743 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python Data Science Handbook by : Jake VanderPlas

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Python Data Cleaning and Preparation Best Practices

Author : Maria Zervou
Publisher : Packt Publishing Ltd
ISBN 13 : 1837632901
Total Pages : 456 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Python Data Cleaning and Preparation Best Practices by : Maria Zervou

Download or read book Python Data Cleaning and Preparation Best Practices written by Maria Zervou and published by Packt Publishing Ltd. This book was released on 2024-09-27 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset Key Features Maximize the value of your data through effective data cleaning methods Enhance your data skills using strategies for handling structured and unstructured data Elevate the quality of your data products by testing and validating your data pipelines Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionProfessionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone. To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio. By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.What you will learn Ingest data from different sources and write it to the required sinks Profile and validate data pipelines for better quality control Get up to speed with grouping, merging, and joining structured data Handle missing values and outliers in structured datasets Implement techniques to manipulate and transform time series data Apply structure to text, image, voice, and other unstructured data Who this book is for Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book.

Building Web Applications with Python and Neo4j

Author : Sumit Gupta
Publisher : Packt Publishing Ltd
ISBN 13 : 178398399X
Total Pages : 184 pages
Book Rating : 4.7/5 (839 download)

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Book Synopsis Building Web Applications with Python and Neo4j by : Sumit Gupta

Download or read book Building Web Applications with Python and Neo4j written by Sumit Gupta and published by Packt Publishing Ltd. This book was released on 2015-07-16 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Py2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms. You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly.