Posts Tagged ‘dataism0mg’

iPad Notebook export for Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else

August 27, 2017

Some quick quotes from
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Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
Lohr, Steve
Citation (MLA): Lohr, Steve. Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. HarperCollins, 2015. Kindle file.
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that I really liked

Each short quote is preceded by the words “Highlight” & indication of the location in the book.

Notebook Export
Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
Lohr, Steve
Citation (MLA): Lohr, Steve. Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. HarperCollins, 2015. Kindle file.
5 The Rise of the Data Scientist
Highlight(pink) – Page 97 · Location 1359
Back in 2001, William S. Cleveland, then a researcher at Bell Labs, wrote a paper he called an “action plan” for essentially redefining statistics as an engineering task. “The altered field,” he wrote, “will be called ‘data science.’” In his paper, Cleveland, who is now a professor of statistics and computer science at Purdue University, described the contours of this new field. Data science, he said, would touch all disciplines of study and require the development of new statistical models, new computing tools, and educational programs in schools and corporations.
6 Data Storytelling: Correlation and Context
Highlight(pink) – Page 111 · Location 1550
Tom Mitchell, chairman of the machine-learning department at Carnegie Mellon, offers two similar sentences as an example of what is most challenging to a knowledge system like NELL. “The girl caught the butterfly with the spots.” And, “The girl caught the butterfly with the net.” A human reader, he notes, inherently understands that girls hold nets, and girls are not usually spotted. So, in the first sentence, “spots” is associated with “butterfly,” and in the second, “net” with “girl.” “That’s obvious to a person, but it’s not obvious to a computer,” Mitchell says.
Highlight(pink) – Page 117 · Location 1637
That seemingly natural division of labor was famously articulated more than a half-century ago by J. C. R. Licklider, a Harvard-trained psychologist and seminal thinker in computing, who sponsored a wave of pioneering computer research in the 1960s as a senior official at the Pentagon’s Advanced Research Projects Agency. In 1960, Licklider wrote “Man-Computer Symbiosis,” a paper that would shape thinking for decades. In it, Licklider stated that the appropriate goal of computing was to “augment” human intelligence rather than substitute for it.
7 Data Gets Physical
Highlight(pink) – Page 124 · Location 1718
The precision agriculture pilot was a joint effort of two companies, IBM and E. & J. Gallo Winery. And it was in good part a collaboration between two men: Hendrik Hamann, a German physicist and researcher at IBM, and Dokoozlian, a native Californian who grew up on a small family vineyard and is Gallo’s chief plant scientist.
8 The Yin and Yang of Behavior and Data
Highlight(pink) – Page 147 · Location 2030
By now, hundreds of thousands of Nest thermostats have collected enough data and Nest’s algorithms have done enough analysis, based on their patterns of activity and energy use, to determine that households can be grouped into four kinds: families with young children; families with older children; empty nesters; and roommates. Within a week after it is installed, the Nest thermostat has observed enough to know what group a household fits into.
Highlight(pink) – Page 154 · Location 2132
Walmart, for example, is renowned for overhauling its supply chain with statistical science.
Highlight(pink) – Page 154 · Location 2135
Haydock speaks of a new “genomics
Highlight(pink) – Page 154 · Location 2135
of business” in the future that will produce a previously unimagined “level of detail in looking at people and companies.” Today, it seems an aspirational analogy, but that is the direction things are heading. 10 The Prying Eyes of Big Data
Highlight(pink) – Page 183 · Location 2516
Freewheeling picture taking was sometimes unwelcome in other public spaces. Highlight(pink) – Page 183 · Location 2516
Kodak cameras were briefly banned at the Washington Monument, according to historian and author David Lindsay. In an editorial, the Hartford Courant bemoaned the loss of privacy as Kodak cameras multiplied.
Highlight(pink) – Page 184 · Location 2520
1960s, a very different technological advance challenged common notions of privacy—the mainframe computer. That’s when the federal government started putting tax returns into the giant computers, and consumer credit bureaus began assembling databases containing the personal financial information on millions of Americans.
Highlight(pink) – Page 184 · Location 2528
Today, our concept of privacy is under threat once again—this time by the technologies of big data. The response, as in the past, will likely be a step-by-step evolution that involves changing some attitudes and changing some rules. As those Kodak cameras spread, for example, people became more comfortable having them around and with snapshot picture-taking in public.
Highlight(pink) – Page 187 · Location 2562
“There is a fundamental gap,” Felten says. “Consumers don’t know what is happening, so they can’t make informed decisions.”
Highlight(pink) – Page 188 · Location 2579
Felten compares the current state of affairs to the digital equivalent of attending a conference with name badges. But instead of names, the people are wearing badges that say, “I’m a diabetic” or “I’m deeply in debt.” “That’s not considered personally identifiable information,” he observes. “But it’s much more sensitive information than your name.” Highlight(pink) – Page 188 · Location 2582
These companies, like Acxiom, Epsilon, and Experian, compile extensive dossiers on millions of individuals and families, tapping data sources that include public records, consumer purchases in physical and online stores, and Web browsing histories.

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A Data Broker Offers a Peek Behind the Curtain – NYTimes.com

August 27, 2017

A Data Broker Offers a Peek Behind the Curtain: site listing what’s compiled about you
http://www.nytimes.com/2013/09/01/business/a-data-broker-offers-a-peek-behind-the-curtain.html MT @robert_schiff #privacy

site = http:aboutthedata.com

Identity Resolution & People-Based Marketing | Acxiom

August 27, 2017

https://www.acxiom.com/

Predix (software) – Wikipedia

August 27, 2017

https://en.wikipedia.org/wiki/Predix_(software)

Randal Bryant – Wikipedia

August 27, 2017

https://en.wikipedia.org/wiki/Randal_Bryant

Jim Gray (computer scientist) – Wikipedia

August 27, 2017

https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)

IBM Watson Developer Cloud

July 18, 2017

https://www.ibm.com/watson/developercloud/

John Tukey – Wikipedia

July 16, 2017

https://en.wikipedia.org/wiki/John_Tukey

T-shaped skills – Wikipedia

July 16, 2017

https://en.wikipedia.org/wiki/T-shaped_skills

Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else: Steve Lohr: 0201562226819: Amazon.com: Books

July 16, 2017