The Data Science Interview: Yun Xiong, Fudan University

The Goal of Data Science is to Study the Phenomena and Laws of Datanature

Yun Xiong is an Associate Professor of Computer Science and the Associate Director of the Center for Data Science and Dataology at Fudan University, Shanghai, China. She received her Ph.D. in Computer and Software Theory from Fudan University in 2008. Her research interests include dataology and data science, data mining, big data analysis, developing effective and efficient data analysis techniques for various applications including finance, economics, insurance, bioinformatics, and sociology. The following is an edited version of our recent email exchange.

How has data science developed in China?    Continue reading

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Big Data Quotes of the Week

“Data is the new science. Big Data holds the answers. Are you asking the right questions?”—Pat Gelsinger, EMC

“You have to realize something about the three of us–we’re all big gamers. And we’re analytical. And competitive. We play those really complicated strategy board games that are supposed to take an hour or two to finish. They take us four or five hours. We just can’t help thinking through every possible move, every possible outcome and side-effect of those moves, and how best to take advantage of a given situation”–Dan Kador, CTO, Keen (on raising venture capital with co-founders Kyle Wild and Ryan Spraetz)

Being a statistician means never having to say you’re sure–Mike Parker ?@Sysparatem    Continue reading

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Top 5 Data Science Influencers: June 24, 2012

1. Gregory Piatetsky, KDnuggests@kdnuggets

2.  Harish Kotadia, Infosys, @HKotadia

3. David Smith, Revolution Analytics@revodavid

4. Gil Press, What’s the Big Data? @GilPress

5. Alex Popescu, MyNoSQL@al3xandru

Source: Traackr

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Big Data Quotes of the Week

“The world is one big data problem. There’s a bit of arrogance in that, and a bit of truth as well.”–Andrew McAfee

“…one of the most exciting parts of the LinkedIn platform and the LinkedIn ecosystem is that the more members we attract, the more deeply they become engaged, the more data is being generated.  And that data can be leveraged to create more relevant experiences for our members and better return on investment for our customers. Data really powers everything that we do.”—Jeff Weiner, LinekdIn   Continue reading

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Top 5 Data Science Influencers: June 17, 2012

1.  Harish Kotadia, Infosys, @HKotadia

2. Gregory Piatetsky, KDnuggests@kdnuggets

3. Avkash Chauhan, Microsoft@avkashchauhan

4. Alex Popescu, MyNoSQL@al3xandru

5. Gil Press, What’s the Big Data? @GilPress

Source: Traackr

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Coursera: Data Science in Education

Monica Rogati in The LinkedIn Blog on Coursera, which offers free online courses: “Coursera’s approach to feedback and assessment is a very interesting application of data science. Tests are either computer-graded or peer-graded — the latter following industry tested crowdsourcing best practices (clear instructions, gold standards, training, qualification tasks assessor agreement monitoring etc.). Peer grading isn’t just treated as a means for scaling — it is part of the learning process. One of Daphne’s charts showed that students significantly improved on subsequent tests after peer- and self-grading. Interestingly, the better students learned even more from self-grading than from grading others…    Continue reading

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The Data Science Interview: Mok Oh, PayPal

To Do Data Science, You Need a Team of Specialists

Currently the Chief Scientist at PayPal, Mok Oh came on board when eBay acquired WHERE, where he was Chief Innovation Officer.  Prior to WHERE, Mok founded EveryScape, a data visualization company.  The following is an edited transcript of our recent phone conversation.

How do you define a data scientist?   Continue reading

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Top 5 Data Science Influencers: June 10, 2012

1.  Harish Kotadia, Infosys, @HKotadia

2. Gregory Piatetsky, KDnuggests@kdnuggets

3. Avkash Chauhan, Microsoft@avkashchauhan

4. Alex Popescu, MyNoSQL@al3xandru

5. David Smith, Revolution Analytics@revodavid

Source: Traackr

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A Very Short History of Big Data

In addition to researching A Very Short History of Data Science, I have also been looking at the history of how data became big. Here I focus on the history of attempts to quantify the growth rate in the volume of data or what has popularly been known as the “information explosion” (a term first used in 1941, according to the OED). The following are the major milestones in the history of sizing data volumes plus other “firsts” or observations pertaining to the evolution of the idea of “big data.”

[An updated version of this timeline is at Forbes.com]

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Big Data Events June-September 2012

Most recent update: June 2, 2012

International Conference on Advancements in Information Technology 2012

June 2-3, Hong Kong

Data Analysis Conference: Tools of the Trade

June 4-5, Atlantic City, New Jersey

TDWI Solution SummitBig Data Analytics for Real-Time Business Advantage

June 4-6, San Diego

13th Annual International Conference on Digital Government Research

June 4-7, University of Maryland, College Park, MD   

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