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Author: Denise Gosnell Publisher: "O'Reilly Media, Inc." ISBN: 1492044024 Category : Computers Languages : en Pages : 471
Book Description
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Author: Denise Gosnell Publisher: "O'Reilly Media, Inc." ISBN: 1492044024 Category : Computers Languages : en Pages : 471
Book Description
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Author: Denise Gosnell Publisher: ISBN: 9781492044062 Category : Database management Languages : en Pages : 0
Book Description
With Early Release ebooks, you get books in their earliest form-the authors' raw and unedited content as they write-so you can take advantage of these technologies long before the official release of these titles. This book will enable you to apply graph thinking to solve complex problems. If you want to learn how to build architectures for extracting value for your domain's complex problems, then this book is for you. You'll learn how to think about your data as a graph, and how to determine if graph technology is right for your application. The book describes techniques for scalable, real-time, and multimodel architectures that solve complex problems, and shows how companies are successfully applying graph thinking in distributed production environments. Authors Denise Koessler Gosnell and Matthias Broecheler also introduce the Graph Schema Language, a set of terminology and visual illustrations to normalize how graph practitioners communicate conceptual graph models, graph schema, and graph database design.
Author: Denise Gosnell Publisher: O'Reilly Media ISBN: 1492044040 Category : Computers Languages : en Pages : 420
Book Description
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Author: David Loshin Publisher: Elsevier ISBN: 9780080920344 Category : Computers Languages : en Pages : 432
Book Description
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Author: Randy Bartlett Publisher: McGraw Hill Professional ISBN: 0071807608 Category : Business & Economics Languages : en Pages : 256
Book Description
Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.
Author: Hui Lin Publisher: CRC Press ISBN: 1351132903 Category : Business & Economics Languages : en Pages : 403
Book Description
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
Author: Gary Hutson Publisher: Packt Publishing Ltd ISBN: 1804619345 Category : Computers Languages : en Pages : 236
Book Description
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.
Author: Allen Rubin Publisher: John Wiley & Sons ISBN: 1119858569 Category : Psychology Languages : en Pages : 308
Book Description
The latest edition of an essential text to help students and practitioners distinguish between research studies that should and should not influence practice decisions Now in its third edition, Practitioner's Guide to Using Research for Evidence-Informed Practice delivers an essential and practical guide to integrating research appraisal into evidence-informed practice. The book walks you through the skills, knowledge, and strategies you can use to identify significant strengths and limitations in research. The ability to appraise the veracity and validity of research will improve your service provision and practice decisions. By teaching you to be a critical consumer of modern research, this book helps you avoid treatments based on fatally flawed research and methodologies. Practitioner's Guide to Using Research for Evidence-Informed Practice, Third Edition offers: An extensive introduction to evidence-informed practice, including explorations of unethical research and discussions of social justice in the context of evidence-informed practice. Explanations of how to appraise studies on intervention efficacy, including the criteria for inferring effectiveness and critically examining experiments. Discussions of how to critically appraise studies for alternative evidence-informed practice questions, including nonexperimental quantitative studies and qualitative studies. A comprehensive and authoritative blueprint for critically assessing research studies, interventions, programs, policies, and assessment tools, Practitioner's Guide to Using Research for Evidence-Informed Practice belongs in the bookshelves of students and practitioners of the social sciences.
Author: Allen Rubin Publisher: John Wiley & Sons ISBN: 111823880X Category : Psychology Languages : en Pages : 384
Book Description
Praise for the previous edition "An extraordinary and important book. Its approach to evidence-based practice (EBP) is very sound, realistic, and generous to the complexities of everyday practice. Reading and using this book is a must." Haluk Soydan, PhD, School of Social Work, University of Southern California "This book has the potential to change practice in the helping professions. Rather than focusing on how to conduct research, Practitioner's Guide to Using Research for Evidence-Based Practice instead shows readers how to understand the literature.... [The] generous use of humor and the inclusion of simple, practice-relevant examples make this book a pleasure to read." Aron Shlonsky, PhD, Faculty of Social Work, University of Toronto "I particularly like the integration of research methods and EBP; this is the book's major innovation in my mind as it allows readers to see the connections between research and practice. [The book] also succeeds by taking very complex EBP principles and explaining them in practical terms." Jeffrey M. Jenson, PhD, Graduate School of Social Work, University of Denver Hands-on guidance for research-informed practice and practice-informed research Now in a second edition, Practitioner's Guide to Using Research for Evidence-Based Practice offers a clinician-oriented approach to appraising and using research as part of the EBP process. This accessible guide presents essential and practical guidance on how to integrate research appraisal into EBP endeavors to determine which interventions, policies, and assessment tools are supported by the best evidence. It introduces: Increased attention to macro-level EBP questions and studies New discussion on defining EBP, including the addition of a transdisciplinary model of EBP More detailed guidance on EBP question formulation and conducting Internet searches, including the PICO framework New content on multivariate designs, including propensity score matching, and on mixed-model and mixed-methods studies
Author: Denise Gosnell
Author: Denise Gosnell
Author: Denise Gosnell
Author: Denise Gosnell
Author: David Loshin
Author: Jesus Barrasa
Author: Randy Bartlett
Author: Hui Lin
Author: Gary Hutson
Author: Allen Rubin
Author: Allen Rubin