MVA: Data Science and ML

Data Science and Machine Learning Essentials
Microsoft Virtual Academy. Level 300
02 November 2015


Data Science with Microsoft SQL Server 2016 – Free eBook

Gartner’s Hype Cycles

Source: Gartner (August 2015)

Gartner’s Hype Cycles

Top IT Trends & Predictions in 2015

Gartner’s 2016 Hype Cycle for Emerging Technologies Identifies Three Key Trends That Organizations Must Track to Gain Competitive Advantage

cited by:
Introduction to Big Data
September 2015
by University of California, San Diego

Distributed innovation

How the U.S. Gets Manufacturing Policy All Wrong
June 2, 2015
By Martin Neil Baily
Bernard L. Schwartz chairman in economic policy development at the Brookings Institution
Washington measures success by the number of jobs, when it should be focused on speeding up automation

distributed innovation, in which crowdsourcing is used to find radical solutions to technical challenges much more quickly and cheaply than with traditional in-house research and development.

…putting robots in place of workers. There will still be good jobs in manufacturing, especially for those with big-data, programming and other specialized skills needed for advanced manufacturing.

It is hard to let go of old ways of thinking, but continuing to chase yesterday’s goals only puts off the inevitable. Instead of dragging out the fight for more manufacturing jobs, we need to focus on speeding up the manufacturing revolution, funding basic science and engineering, and ensuring that tech talent and best practice companies want to produce in the U.S.

Training Data Scientists (2014)

Structure Data 2014: How Will We Train Data Scientists of the Future?
GIGAOM, April 13, 2014
AnnaLee Saxenian — Professor and Dean, UC Berkeley

12:50 the core that everybody would agree to is pretty small:

  • statistics
  • computer science programming
  • Big Data tools

Introduction to Big Data
September 2015
by University of California, San Diego

Our emotional state biases our expectations for the future

Mining Books To Map Emotions Through A Century
April 01, 2013

“Generally speaking, the usage of these commonly known emotion words has been in decline over the 20th century,” Bentley says. We used words that expressed our emotions less in the year 2000 than we did 100 years earlier — words about sadness and joy and anger and disgust and surprise.

In fact, there is only one exception that Bentley and his colleagues found: fear. “The fear-related words start to increase just before the 1980s,” he says.

this method — mining vast amounts of written language — is incredibly promising.

language analysis seems so promising to him — as a new window that might offer a different, maybe even more objective, view into our culture. Because, he says, it’s difficult for people today to guess the emotions of people of different times.

Our current emotional state completely biases our memories of the past and our expectations for the future,” Pennebaker says. “And, using these language samples, we are able to peg how people are feeling over time.
That’s what I love about it as a historical marker, so we can get a sense of how groups of people — or entire cultures — might have felt 10 years ago, or 100 years ago.”

see also:

We’ve become loose in applying the term “mental disorder” to …