Tufte Courses

Notable always liked Tufte's work.   Attended a few courses in the big enterprise.

ANALYZING/PRESENTING    DATA/INFORMATION:

AN ONLINE VIDEO COURSE TAUGHT BY EDWARD TUFTE

EDWARD TUFTE COURSE REVIEWS + REGISTRATION INFORMATION

ET Books - Covers

"Best single-day class ever." TAOSECURITY       "The da Vinci of data." THE NEW YORK TIMES

"In university halls and conference centers, Tufte's appeal crackles. Fans spend the day looking at art and information through Tufte's eyes, as he walks them through images and analysis of his books. In 4 books and popular auditorium gigs, he teaches by visual example. Next to a bad example of a graph, he positions a sublimely clear treatment, often using the same data. Tufte's work is relevant to anyone who needs to write or present information clearly, from business executives to students. About 10 years ago, The New York Times crowned Tufte the "da Vinci of data." A more fitting title might be the "Galileo of graphics." Where da Vinci is remembered as an inventor of new technologies, Galileo put right our understanding of the solar system by positioning the sun at its center. Tufte, who owns a handful of nearly 400-year-old first editions by Galileo considers the early scientist a master of analytical design." BLOOMBERG

"The Information Sage: Meet Edward Tufte, the graphics guru to the power elite who is revolutionizing how we see data." Edward Tufte's many goverment data presentations in Washington, DC, including his presidential appointment to the Recovery Independent Advisory Panel and work on recovery.org."" THE WASHINGTON MONTHLY

"One visionary day. Few speak as eloquently as Edward Tufte, whose theories of information design not only illuminate, they inspire. In a full-day seminar, Tufte, author of the classic The Visual Display of Quantitative Information, uses maps, graphs, charts, and tables to communicate what prose alone cannot. For information designers Tufte's work is a model of clarity and craftsmanship. Given that the heart of his enterprise is statistics (of which he's a professor at Yale), one might worry about "lognormal distributions" and "trimetric projections." This would be a mistake. Tufte keeps jargon to a minimum. His insights lead to new levels of understanding both for creators and viewers of visual display. What makes Tufte most persuasive are his works themselves: His books and his seminar embody his belief that "good design is clear thinking made visible." WIRED

"Ivy League Rock and Roll: Edward Tufte, the world's most renowned visualization expert, holds legendary information design courses. I recently was among the hundreds that flock to each of his live performances. What an experience! Tufte's seminars are legendary. Anyone who deals with data and visualization on a professional level knows his books. Today in Washington's Crystal Forum many participants come from government agencies and military institutions. There are also several students whose majors range from information technology and graphic design to economics, biology and medicine. Some, balancing notebooks on their laps, will take detailed notes so that they can fully absorb Tufte's messages later at home. He gently takes an awe-striking original from Galileo Galilei or a centuries-old copy of Euclid's scripts and proceeds to carry it down the aisle. Later, one of his assistants will also walk through the auditorium with one of these precious books in hand." NICOLAS BISSANTZ  .... ' 

The Future is Big Graphs

Like the thought.    But how do we define big here? Complete?

The Future Is Big Graphs: A Community View on Graph Processing Systems

By Sherif Sakr, Angela Bonifati, Hannes Voigt, Alexandru Iosup, Khaled Ammar, Renzo Angles, Walid Aref, Marcelo Arenas, Maciej Besta, Peter A. Boncz, Khuzaima Daudjee, Emanuele Della Valle, Stefania Dumbrava, Olaf Hartig, Bernhard Haslhofer, Tim Hegeman, Jan Hidders, Katja Hose, Adriana Iamnitchi, Vasiliki Kalavri, Hugo Kapp, Wim Martens, M. Tamer Özsu, Eric Peukert, Stefan Plantikow, Mohamed Ragab, Matei R. Ripeanu, Semih Salihoglu, Christian Schulz, Petra Selmer, Juan F. Sequeda, Joshua Shinavier

Communications of the ACM, September 2021, Vol. 64 No. 9, Pages 62-71    10.1145/3434642

Graphs are, by nature, 'unifying abstractions' that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?

We are witnessing an unprecedented growth of interconnected data, which underscores the vital role of graph processing in our society. Instead of a single, exemplary ("killer") application, we see big graph processing systems underpinning many emerging but already complex and diverse data management ecosystems, in many areas of societal interest.a

To name only a few recent, remarkable examples, the importance of this field for practitioners is evidenced by the large number (more than 60,000) of people registeredb to download the Neo4j book Graph Algorithmsc in just over one-and-a-half years, and by the enormous interest in the use of graph processing in the artificial intelligence (AI) and machine learning (ML) fields.d Furthermore, the timely Graphs 4 COVID-19 initiativee is evidence of the importance of big graph analytics in alleviating the pandemic.

Academics, start-ups, and even big tech companies such as Google, Facebook, and Microsoft have introduced various systems for managing and processing the growing presence of big graphs. Google's PageRank (late 1990s) showcased the power of Web-scale graph processing and motivated the development of the MapReduce programming model, which was originally used to simplify the construction of the data structures used to handle searches, but has since been used extensively outside of Google to implement algorithms for large-scale graph processing.

Motivated by scalability, the 2010 Google Pregel "think-like-a-vertex" model enabled distributed PageRank computation, while Facebook, Apache Giraph, and ecosystem extensions support more elaborate computational models (such as task-based and not always distributed) and data models (such as diverse, possibly streamed, possibly wide-area data sources) useful for social network data. At the same time, an increasing number of use cases revealed RDBMS performance problems in managing highly connected data, motivating various startups and innovative products, such as Neo4j, Sparksee, and the current Amazon Neptune. Microsoft Trinity and later Azure SQL DB provided an early distributed database-oriented approach to big graph management.   ... '