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What is data science?
June 2, 2010 | - O'Reilly Radar | by Mike Loukides |.
visualization  statistics  analytics  data  data_scientists 
september 2012 by jerryking
Data Scientist: The Sexiest Job of the 21st Century
October 2012 | Harvard Business Review | by Thomas H. Davenport and D.J. Patil
HBR  data_scientists  21st._century  career_paths 
september 2012 by jerryking
So Much Data From Smart Meters, but Who Can Analyze It? -
July 11, 2012, 12:27 amComment
So Much Data From Smart Meters, but Who Can Analyze It?
july 2012 by jerryking
Jake Porway, Data Scientist Information, Facts, News, Photos -- National Geographic
Data scientist Jake Porway (Ph.D.) is a matchmaker. He sees social change organizations working to make the world a better place, collecting mountains of data, but lacking skills and resources to use that information to advance their mission. He sees data scientists with amazing skills and cutting-edge tools, eager to use their talent to accomplish something meaningful, yet cut off from channels that allow them to do so. He sees governments ready to make data open and available, but disconnected from people who need it. For Porway, it's a match waiting to happen and the reason he founded DataKind (formerly Data Without Borders). It connects nonprofits, NGOs and other data-rich social change organizations with data scientists willing to donate time and knowledge to solve social, environmental and community problems. Ultimately, he wants to build a globally connected network of dedicated experts who can be deployed at a moment's notice to tackle any big data science task worldwide
data_scientists  DataKind  data  match-making  haystacks  PhDs  open_data  nonprofit  NGOs  volunteering 
july 2012 by jerryking
Talent Shortage Looms Over Big Data -
April 29, 2012 | WSJ | By BEN ROONEY

Big Data's Big Problem: Little Talent

"A significant constraint on realizing value from Big Data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from Big Data," the report said. "We project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively." What the industry needs is a new type of person: the data scientist.....Hilary Mason, chief scientist for the URL shortening service, says a data scientist must have three key skills. "They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece."

It is this ability to turn data into information into action that presents the most challenges. It requires a deep understanding of the business to know the questions to ask. The problem that a lot of companies face is that they don't know what they don't know, as former U.S. Defense Secretary Donald Rumsfeld would say. The job of the data scientist isn't simply to uncover lost nuggets, but discover new ones and more importantly, turn them into actions. Providing ever-larger screeds of information doesn't help anyone.

One of the earliest tests for biggish data was applying it to the battlefield. The Pentagon ran a number of field exercises of its Force XXI—a device that allows commanders to track forces on the battlefield—around the turn of the century. The hope was that giving generals "exquisite situational awareness" (i.e. knowing everything about everyone on the battlefield) would turn the art of warfare into a science. What they found was that just giving bad generals more information didn't make them good generals; they were still bad generals, just better informed.

"People have been doing data mining for years, but that was on the premise that the data was quite well behaved and lived in big relational databases," said Mr. Shadbolt. "How do you deal with data sets that might be very ragged, unreliable, with missing data?"

In the meantime, companies will have to be largely self-taught, said Nick Halstead, CEO of DataSift, one of the U.K. start-ups actually doing Big Data. When recruiting, he said that the ability to ask questions about the data is the key, not mathematical prowess. "You have to be confident at the math, but one of our top people used to be an architect".
data_scientists  massive_data_sets  talent_management  talent  Pentagon  SecDef  limitations  shortages  McKinsey  war_for_talent  recruiting  Colleges_&_Universities  situational_awareness  questions  Donald_Rumsfeld  asking_the_right_questions 
june 2012 by jerryking
Data as a Renewable Commodity and Profit Center — Jason Kolb dot Com
June 6, 2011 | Jason | By Jason Kolb.

One of the reasons I love data is because there’s so much potential for mining real value from it, especially when you combine it with other, new data sources. In fact it acts a lot like a traditional commodity such as copper or wool in that someone produces it, and then someone else buys the raw material and makes something new from it. It’s unique from traditional commodities, however, in that it doesn’t get used up at all when it’s used to create something new–this makes it particularly interesting from an economic point of view.

In addition, anyone can make it, it doesn’t get used up, and the industry of using data to create new and valuable things is still so young and ripe for profit-making. In fact I think it’s one of the areas that America needs to focus on if its economy is to recover because for the most part it’s still virgin territory and it’s going to create a lot of economic value. What I really don’t want to see is foreign companies being the first to capitalize on the data as that would suck most of the value out of our economy, just what we don’t need right now.
data  commercialization  renewable  commodities  metadata  Factual  Infochimps  data_scientists  information_sources 
june 2012 by jerryking
The Trouble with Big Data
May 5, 2012 | | What's The Big Data?| GilPress

“With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real… On net, having a degree in math, economics, AI, etc., isn’t enough. Tool expertise isn’t enough. You need experience in solving real world problems, because there are a lot of important limitations to the statistics that you learned in school. Big data isn’t about bits, it’s about talent.”.....The “talent” of “understanding the problem and the data applicable to it” is what makes a good scientist: The required skepticism, the development of hypotheses (models), and the un-ending quest to refute them, following the scientific method that has brought us remarkable progress over the course of the last three hundred and fifty years.
in_the_real_world  massive_data_sets  blogs  skepticism  challenges  problems  problem_solving  expertise  statistics  talent  spurious  data_quality  data_scientists  haystacks  correlations  limitations 
june 2012 by jerryking
Twitter sells your feed to Big Data - The Globe and Mail
Mar. 01, 2012 |Reuters| by Mitch Lipka.

Boulder, Colorado-based Gnip Inc. and DataSift Inc., based in the U.K. and San Francisco, are licensed by Twitter to analyze archived tweets and basic information about users, like geographic location. DataSift announced this week that it will release Twitter data in packages that will encompass the last two years of activity for its customers to mine, while Gnip can go back only 30 days.
Twitter  Gnip  massive_data_sets  data_scientists  commercialization  DataSift  social_data 
march 2012 by jerryking
PeteSearch: How to turn data into money
October 20, 2010 by Pete Warden. The most important unsolved
question for Big Data startups is how to make money. Here's a hierarchy
showing the stages from raw data to cold, hard cash:
(1) Data. You have a bunch of files containing info. you've gathered,
way too much for any human to ever read. You know there's a lot of
useful stuff in there though, but you can talk until you're blue in the
face & the people with the checkbooks will keep them closed. The
data itself, no matter how unique, is low value, since it will take
somebody else a lot of effort to turn it into something they can use to
make $. (2) Charts. Take that massive deluge of data and turn it into
some summary tables & simple graphs. You want to give an unbiased
overview of the info., so the tables & graphs are quite detailed.
This makes a bit more sense to the potential end-users, they can at
least understand what it is you have, and start to imagine ways they
could use it. (3) Reports; (4) Recommendations.
analysis  commercialization  data  data_driven  data_marketplaces  data_scientists  entrepreneurship  hierarchies  ideas  InfoChimps  massive_data_sets  monetization  value_creation  visualization 
july 2011 by jerryking
Data-as-a-Service: Factual, InfoChimps & Google Squared
Oct. 20, 2010, By Imran Ali . Do you have unique datasets in
your biz. that could be valuable to others?...dB apps have been
curiously absent from the mix of web worker productivity tools...a new
generation of tools are providing this functionality. DaaS providers are
emerging enabling users to create, manage & publish specialized
datasets, providing both authoring tools & opportunities to
participate in a web of data, not just of pgs...Factual bills itself as
an “open data repository” where users can upload & create datasets,
as well as add data hosted by Factual to their own sites &
apps...InfoChimps positions itself as a “data mktplace” enabling
publishers & owners of datasets to charge for their usage.
Publishers can offer free & paid datasets, charging either for API
access or for making them downloadable. Some datasets are organized into
collections from particular organizations,e.g. Wikipedia &
==> InfoChimps allows orgs. to outsource mgmt. of their open data
What's an example of a company creating a valuable dataset from scratch?
DaaS  Infochimps  Factual  data  Google_Squared  Freshbooks  massive_data_sets  databases  data_scientists  commercialization 
july 2011 by jerryking
Big Thoughts on Big Data: Infochimps
Mar. 2, 2011, | Gigaom -- Cloud Computing News | By Stacey Higginbotham
data  massive_data_sets  Infochimps  DaaS  databases  Freshbooks  data_scientists 
july 2011 by jerryking
Data markets aren't coming. They're already here
26 January 2011 | O'Reilly Radar| by Julie Steele.

Jud Valeski is cofounder and CEO of Gnip, a social media data provider
that aggregates feeds from sites like Twitter, Facebook, Flickr,
delicious, and others into one API.

Jud will be speaking at Strata next week on a panel titled "What's Mine
is Yours: the Ethics of Big Data Ownership."
Find out more about growing business of data marketplaces at a "Data
Marketplaces" panel with Ian White of Urban Mapping, Peter Marney of
Thomson Reuters and Dennis Yang of Infochimps.

What do you wish more people understood about data markets and/or the
way large datasets can be used?

Jud Valeski: First, data is not free, and there's always someone out
there that wants to buy it. As an end-user, educate yourself with how
the content you create using someone else's service could ultimately be
used by the service-provider. Second, black markets are a real problem,
and just because "everyone else is doing it" doesn't mean it's okay.
markets  data  analytics  massive_data_sets  digital_economy  content_creators  black_markets  Infochimps  Gnip  Thomson_Reuters  commercialization  data_scientists  data_marketplaces  social_data  financial_data 
may 2011 by jerryking
Slipstream: When the Data Struts Its Stuff | Forex Mentor
April 2, 2011

They are computer scientists, statisticians, graphic designers,
producers and cartographers who map entire oceans of data and turn them
into innovative visual displays, like rich graphs and charts, that help
both companies and consumers cut through the clutter. These gurus of
visual analytics are making interactive data synonymous with attractive

“Statistics,” says Dr. Hans Rosling, “is now the sexiest subject
around.” ...the goal of information visualization is not simply to
represent millions of bits of data as illustrations. It is to prompt
visceral comprehension, moments of insight that make viewers want to
learn more....“The purpose of visualization,” says Ben Shneiderman,
founding director of the Human-Computer Interaction Laboratory at the
University of Maryland, “is insight, not pictures.”
Hans_Rosling  visualization  infographics  data  information_overload  statistics  Freshbooks  data_scientists  insights 
april 2011 by jerryking
For Today’s Graduate, Just One Word - Statistics -
Aug. 5, 2009 | NYT | By STEVE LOHR. “We’re entering a world
where everything can be monitored and measured,” said Erik Brynjolfsson,
an economist and director of MIT’s Center for Digital Business. “But
the big problem is the ability of man to use, analyze and make sense of
the data.”" The rich lode of Web data has its perils. Its sheer vol. can
easily overwhelm statistical models. Statisticians caution that strong
correlations of data do not necessarily prove a cause-and-effect link.
E.g., in the late 1940s, before there was a polio vaccine, public health
experts noted that polio cases increased in step with the consumption
of ice cream and soft drinks, says David A. Grier, a historian and
statistician at GWU. Eliminating such treats was recommended as part of
an anti-polio diet. It turned out that polio outbreaks were most common
in the hot mths of summer, when people ate more ice cream, showing only
an association. The data explosion magnifies longstanding issues in
Steve_Lohr  Hal_Varian  statistics  career_paths  haystacks  analytics  Google  data  Freshbooks  information_overload  data_scientists  Erik_Brynjolfsson  measurements  sense-making  massive_data_sets  correlations  causality 
june 2010 by jerryking
Computer Science Loses to Math in New Hiring Formula -
APRIL 8, 2010 | Wall Street Journal | By JESSICA E.
VASCELLARO. New Hiring Formula Values Math Pros. Region's Employers Seek
Statistical Experts Over Computer-Science Generalists.
hiring  silicon_valley  statistics  competingonanalytics  pattern_recognition  Jessica_E._Vascellaro  data_scientists 
april 2010 by jerryking
The Three Sexy Skills of Data Geeks : Dataspora Blog
Hal Varian, Google’s Chief Economist, was interviewed a few
months ago, and said the following in the McKinsey Quarterly:
“The sexy job in the next ten years will be statisticians… The ability
to take data—to be able to understand it, to process it, to extract
value from it, to visualize it, to communicate it—that’s going to be a
hugely important skill.” Put All Three Skills Together: Sexy. Thus with
the Age of Data upon us, those who can model, munge (data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics) , and visually
communicate data — call us statisticians or data geeks — are a hot
data_wrangling  statistics  visualization  data  analytics  career  business  Information_Rules  Hal_Varian  data_scientists 
july 2009 by jerryking
Math Will Rock Your World
JANUARY 23, 2006 | Business Week | Stephen Baker

The world is moving into a new age of numbers. Partnerships between mathematicians and computer scientists are bulling into whole new domains of business and imposing the efficiencies of math. Look at where the mathematicians are now. They're helping to map out advertising campaigns, they're changing the nature of research in newsrooms and in biology labs, and they're enabling marketers to forge new one-on-one
relationships with customers.
advertising  Stephen_Baker  competingonanalytics  data  data_mining  mathematics  analytics  algorithms  data_scientists  marketing 
april 2009 by jerryking

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