A good visualization can communicate the nature and potential impact of ideas more powerfully than any other form of communication.
For a long time, "dataviz" was left to specialists--data scientists and professional designers. No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could. The Harvard Business Review Good Charts Collection brings together two popular books to help you become more sophisticated in understanding and using dataviz to communicate your ideas and advance your career.
In Good Charts, dataviz maven and Harvard Business Review editor Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. He lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping.
In Good Charts Workbook, Berinato extends the usefulness of Good Charts by putting theory into practice. He leads readers step-by-step through several example datasets and basic charts, providing space to practice the Good Charts talk-sketch-prototype process for improving those charts. Examples include a "Discussion Key" showing how to approach the challenge and why. Each challenge focuses on a different, common visualization problem such as simplification, storytelling, creating conceptual charts, and many others.
The Harvard Business Review Good Charts Collection is your go-to resource for turning plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.
Автор: Xiang Ren, Jiawei Han Название: Mining Structures of Factual Knowledge from Text: An Effort-Light Approach ISBN: 1681733943 ISBN-13(EAN): 9781681733944 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 102570.00 T Наличие на складе: Невозможна поставка. Описание: The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora.Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.
Автор: Steve Hoberman Название: Data Modeling Master Class Training Manual: Steve Hobermans Best Practices Approach to Understanding & Applying Fundamentals Through Advanced Modeling Techniques ISBN: 193550441X ISBN-13(EAN): 9781935504412 Издательство: Gazelle Book Services Рейтинг: Цена: 284550.00 T Наличие на складе: Невозможна поставка. Описание: System science and engineering is a field that covers a wide spectrum of modern technology. A system can be seen as a collection of entities and their interrelationships, which forms a whole greater than the sum of the entities and interacts with people, organizations, cultures and activities and the interrelationships among them. Systems composed of autonomous subsystems are not new, but the increased complexity of modern technology demands ever more reliable, intelligent, robust and adaptable systems to meet evolving needs. This book presents papers delivered at the International Conference on System Science and Engineering ICSSE2015,
Автор: Xiang Ren, Jiawei Han Название: Mining Structures of Factual Knowledge from Text: An Effort-Light Approach ISBN: 1681733927 ISBN-13(EAN): 9781681733920 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 82230.00 T Наличие на складе: Невозможна поставка. Описание: The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora.Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
