• “The goal is to turn data into information, and information into insight.”– Carly Fiorina, former CEO, Hewlett-Packard Co.

  • “Data is the new science. Big data holds the answers.”– Pat Gelsinger, CEO, EMC

  • “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”– Atul Butte, Stanford

  • “You can have data without information, but you cannot have information without data.”– Daniel Keys Moran, Programmer and Author

  • “Information is the oil of the 21st century, and analytics is the combustion engine.”– Peter Sondergaard, SVP, Gartner Research

  • I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.”– Hal Varian, Chief Economist, Google

  • "Data scientist is just a sexed up word for statistician."- Nate Silver, editor-in-chief of ESPN's FiveThirtyEight blog and a Special Correspondent for ABC News.

  • "Data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician."- Robert Rodriguez, President, American Statistical Association

  • "Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others."- Mike Loukides, VP, O’Reilly Media

  • “Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data.”- Michael O'Connell, Sr. Director of Analytics, TIBCO

  • "By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights."-Monica Rogati,  VP for Data, Jawbone

  • "A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product."- Hillary Mason, Data Scientist, Accel, Scientist Emeritus, bitly, co-founder, HackNY

  • "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."2011 McKinsey report (see resources below)

  • "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."2011 McKinsey report (see resources below)

  • "The data scientist was called, only half-jokingly, 'a caped superhero.'"Ben Rooney, Wall Street Journal

  • "By 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge."- 2013 McKinsey report (see resources below)

  • "Autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives."Neil Raden, CEO & Principal Analyst, Hired Brains Research

  • “Statistics are ubiquitous in life, and so should be statistical reasoning.”Alan Blinder, former Federal Reserve vice chairman and Princeton academic

  • "My organization is slowly embracing predictive analytics outside of our environmental concerns, so this course contributes directly to strengthening my R toolkit!"Dr. Jonathan Jenkins

  • "More and more medical students are submitting systematic reviews and associated meta-analyses, to meet research exposure requirements, in the Basic Sciences Programs of their medical school education, here in the Caribbean. The course provided me with a unique and much appreciated opportunity to learn much more about the technique, its underlying principles, and how to correctly apply the tool whenever evaluating a collection of primary studies. Time and energies committed to the course have been very well spent."William Keller

  • "After trying to learn meta-analysis from various texts (e.g., Hunter et al., 2004; Cooper et al., 2009 and others), I found the Borenstein text and CMA software to be *extremely* user-friendly.  It made the selection of fixed-effect or random-effects models much easier to understand, and the presentation of the math was very straightforward.  If I could write a book, I would want to do it this way.  Each chapter was short and to the point.  Not a lot of extras, but a sufficient number of worked examples to make it work.  The software is very easy to use, and I appreciate the continual improvement that seems to be made.  You and your colleagues have done something incredible here."Chad Briggs

  • "It will help me gain confidence with conducting data analysis using R.  I have tried a few R MOOCs in the past which helped me start out using R but I had not found a comprehensive intro to R that I could use more broadly - this course really succeeds with accomplishing that."Dalia Estrada-Pintel

  • "This course has given me a much better understanding of biostatistics and will allow me to gain more from my campus based studies,"Leighton Anderson

  • "This course supplied a solid foundation for identifying statistical outliers in demand patterns for business planning purposes.  In addition, it has deepened my understanding of statistical safety stock calculations & inventory level decision making."James Migliano

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