Gephi Cookbook

2015-05-27 Computers

Author : Devangana Khokhar
Release : 2015-05-27
Genre : Computers
Kind : eBook
Book Rating : 413/5 ( reviews)

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Book Synopsis Gephi Cookbook by : Devangana Khokhar

Download or read book Gephi Cookbook written by Devangana Khokhar. This book was released on 2015-05-27. Available in PDF, EPUB and Kindle. Book excerpt: If you want to learn network analysis and visualization along with graph concepts from scratch, then this book is for you. This is ideal for those of you with little or no understanding of Gephi and this domain, but will also be beneficial for those interested in expanding their knowledge and experience.

Data Science for Web3

2023-12-29 Computers

Author : Gabriela Castillo Areco
Release : 2023-12-29
Genre : Computers
Kind : eBook
Book Rating : 587/5 ( reviews)

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Book Synopsis Data Science for Web3 by : Gabriela Castillo Areco

Download or read book Data Science for Web3 written by Gabriela Castillo Areco. This book was released on 2023-12-29. Available in PDF, EPUB and Kindle. Book excerpt: Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internet Key Features Build a deep understanding of the fundamentals of blockchain analytics Extract actionable business insights by modeling blockchain data Showcase your work and gain valuable experience to seize opportunities in the Web3 ecosystem Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience.

Decision Making in Systems Engineering and Management

2022-10-25 Computers

Author : Patrick J. Driscoll
Release : 2022-10-25
Genre : Computers
Kind : eBook
Book Rating : 421/5 ( reviews)

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Book Synopsis Decision Making in Systems Engineering and Management by : Patrick J. Driscoll

Download or read book Decision Making in Systems Engineering and Management written by Patrick J. Driscoll. This book was released on 2022-10-25. Available in PDF, EPUB and Kindle. Book excerpt: DECISION MAKING IN SYSTEMS ENGINEERING AND MANAGEMENT A thoroughly updated overview of systems engineering management and decision making In the newly revised third edition of Decision Making in Systems Engineering and Management, the authors deliver a comprehensive and authoritative overview of the systems decision process, systems thinking, and qualitative and quantitative multi-criteria value modeling directly supporting decision making throughout the system lifecycle. This book offers readers major new updates that cover recently developed system modeling and analysis techniques and quantitative and qualitative approaches in the field, including effective techniques for addressing uncertainty. In addition to Excel, six new open-source software applications have been added to illustrate key topics, including SIPmath Modeler Tools, Cambridge Advanced Modeller, SystemiTool2.0, and Gephi 0.9.2. The authors have reshaped the book’s organization and presentation to better support educators engaged in remote learning. New appendices have been added to present extensions for a new realization analysis technique and getting started steps for each of the major software applications. Updated illustrative examples support modern system decision making skills and highlight applications in hardware, organizations, policy, logistic supply chains, and architecture. Readers will also find: Thorough introductions to working with systems, the systems engineering perspective, and systems thinking In-depth presentations of applied systems thinking, including holism, element dependencies, expansive and contractive thinking, and concepts of structure, classification, and boundaries Comprehensive explorations of system representations leading to analysis In-depth discussions of supporting system decisions, including the system decision process (SDP), tradespace methods, multi-criteria value modeling, working with stakeholders, and the system environment Perfect for undergraduate and graduate students studying systems engineering and systems engineering management, Decision Making in Systems Engineering and Management will also earn a place in the libraries of practicing system engineers and researchers with an interest in the topic.

R Data Analysis Cookbook

2017-09-20 Computers

Author : Kuntal Ganguly
Release : 2017-09-20
Genre : Computers
Kind : eBook
Book Rating : 319/5 ( reviews)

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Book Synopsis R Data Analysis Cookbook by : Kuntal Ganguly

Download or read book R Data Analysis Cookbook written by Kuntal Ganguly. This book was released on 2017-09-20. Available in PDF, EPUB and Kindle. Book excerpt: Over 80 recipes to help you breeze through your data analysis projects using R About This Book Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Who This Book Is For This book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed. What You Will Learn Acquire, format and visualize your data using R Using R to perform an Exploratory data analysis Introduction to machine learning algorithms such as classification and regression Get started with social network analysis Generate dynamic reporting with Shiny Get started with geospatial analysis Handling large data with R using Spark and MongoDB Build Recommendation system- Collaborative Filtering, Content based and Hybrid Learn real world dataset examples- Fraud Detection and Image Recognition In Detail Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios. Style and Approach Hands-on recipes to walk through data science challenges using R Your one-stop solution for common and not-so-common pain points while performing real-world problems to execute a series of tasks. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf

Graph Machine Learning

2021-06-25 Computers

Author : Claudio Stamile
Release : 2021-06-25
Genre : Computers
Kind : eBook
Book Rating : 755/5 ( reviews)

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Book Synopsis Graph Machine Learning by : Claudio Stamile

Download or read book Graph Machine Learning written by Claudio Stamile. This book was released on 2021-06-25. 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.

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