“People refer to neural networks as just ‘another tool in your machine learning toolbox’…. Unfortunately, this interpretation completely misses the forest for the trees. Neural networks are not just another classifier; they represent the beginning of a fundamental shift in how we write software. They are Software 2.0.” Andrej Karpathy ( 2017 )
“Today knowledge has power. It controls access to opportunity and advancement.” Peter Drucker
“Facts speak louder than statistics.” Mr. Justice Streatfield ( 1950 )
“We have chosen R because:
– It is extremely flexible, allowing us to do data collection, data management, exploratory data analysis, and other essential non-modeling tasks.
– Manipulating matrices is easy.
– It has sophisticated tools for modeling investment returns and for analyzing and presenting results of simulations. And it has great tools for visualizing results.
– The work can be completely open and reproducible, which is essential to the success of this project.” Don Boyd ( September 02, 2014 )
“Analytic and decision making have to be probabilistic; and the system and application has to be conscious of what is “good enough” and not fail in the absence of perfect behavior.” Prasant Misra, Yogesh Simmhan, Jay Warrior ( 03.02.2015 )
“You can have data without information, but you cannot have information without data.” Daniel Keys Moran
“Learning how to do data science is like learning to ski. You have to do it.” Claudia Perlich
“Facts speak louder than statistics.” Eric Colson, Brad Klingenberg, Jeff Magnusson ( March 31, 2015 )
“At one time we had wisdom, but little knowledge. Now we have a great deal of knowledge, but do we have enough wisdom to deal with that knowledge? I define wisdom as the capacity to make retrospective judgments prospectively. I think these are human qualities, human attributes that need to be brought out, need to be drawn upon, need to be valued.” Jonas Salk ( 1991 )
“Two decades ago the folks who prepared our reports, graphs, and visualizations were ‘data analysts’ who knew how to extract data from relational data warehouses and run it through reporting and visualization tools like Crystal Reports. Ten years ago, predictive models were built by ‘predictive modelers’ who understood both the extraction and preparation of the data as well as the specialized predictive analytic tools like SAS and SPSS that allowed them to prepare predictive models. In the last few years, Gartner now declares that we need ‘data scientist’ who have all the above skills but also understand the complexities of the new NoSQL databases like Hadoop and can marry data from many sources and types together to produce useful and profitable predictive models. The requirement for broader and deeper skills is real and must factor into any business decision to build in-house capacity, as well as vetting potential consultants.” William Vorhies ( August 19, 2014 )