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Author: John Dienst
Publisher: Aerofax
ISBN:
Category : History
Languages : en
Pages : 68
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Book Description
Author: John Dienst
Publisher: Aerofax
ISBN:
Category : History
Languages : en
Pages : 68
Get Book Here
Book Description
Author: Jonathan Schwabish
Publisher: Columbia University Press
ISBN: 0231550154
Category : Computers
Languages : en
Pages : 464
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Book Description
Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
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Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
ISBN: 1119002265
Category : Mathematics
Languages : en
Pages : 284
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Book Description
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
Author: Nancy Love
Publisher: Corwin Press
ISBN: 1071841432
Category : Education
Languages : en
Pages : 409
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Book Description
"This book offers a compelling message of hope and resolve. The authors′ three-year journey in a multiplicity of diverse, underperforming, high-poverty schools across the nation has resulted in a treasure chest of knowledge and experiences about how to professionally develop data coaches in ways that benefit some of our most underserved students. This book provides powerful resources to those who have the belief, passion, and desire for implementing collaborative data inquiries in schools and districts." —From the Foreword by Ruth S. Johnson Use data as an effective tool for school change and improvement! How can data coaches create a collaborative culture in which data is used continuously and effectively to improve teaching and learning? The Data Coach′s Guide to Improving Learning for All Students provides detailed guidance for helping schools move away from unproductive data practices and toward examining data as a catalyst for systematic and continuous improvement in instruction and student learning. To help both current and aspiring data coaches facilitate school-based data teams and lead teachers in collaborative inquiry, the authors demonstrate a data model that has been field-tested and proven to be effective in: Narrowing achievement gaps between students in all content areas and grade levels Achieving strong, steady gains in local and state assessments in mathematics, science, and reading Using data as a springboard for powerful conversations about race/ethnicity, class, educational status, gender, and language differences Developing shared values and a vision for creating a high-performing, data-informed school culture This culturally responsive resource benefits staff developers, teachers, and administrators interested in creating change through effective data practices, and includes a CD-ROM keyed to the book, with templates, handouts, PowerPoint slides, resources, and sample goals and agendas.
Author: Michael R. Berthold
Publisher: Springer Nature
ISBN: 3030445844
Category : Computers
Languages : en
Pages : 601
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Book Description
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
Author: Mark Needham
Publisher: "O'Reilly Media, Inc."
ISBN: 1492047635
Category : Computers
Languages : en
Pages : 297
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Book Description
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
Author: Klaus-Dieter Schewe
Publisher: Springer Nature
ISBN: 3030320650
Category : Computers
Languages : en
Pages : 353
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Model and Data Engineering, MEDI 2019, held in Toulouse, France, in October 2019. The 11 full papers and 7 short papers presented in this book were carefully reviewed and selected from 41 submissions. The papers cover broad research areas on both theoretical, systems and practical aspects. Some papers include mining complex databases, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems.
Author: Fusheng Wang
Publisher: Springer
ISBN: 331941576X
Category : Computers
Languages : en
Pages : 206
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Book Description
This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data Management and Analytics for Medicine and Healthcare, DMAH 2015, held at Waikoloa, Hawaii, USA on August 31 and September 4, 2015, in conjunction with the 41st International Conference on Very Large Data Bases, VLDB 2015. The 9 revised full papers presented together with 5 invited papers and 1 extended abstract were carefully reviewed and selected from 22 initial submissions. The papers are organized in topical sections on information retrieval and data analytics for electronic medical records; data management and visualization of medical data; biomedical data sharing and integration; medical imaging analytics; and big-graphs online querying.
Author: Robert L. Harris
Publisher: Oxford University Press, USA
ISBN: 9780195135329
Category : Business & Economics
Languages : en
Pages : 450
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Book Description
Visual tools for analysing, managing and communicating.
