“I almost feel that folks in data science [excluding statisticians] are suddenly realizing that this kind of work is not new and are desperately looking for ways to justify a distinction.” Thomas Speidel

“Data scientists are kind of like the new Renaissance folks, because data science is inherently multidisciplinary.” John Foreman

“Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data. You can see the dependencies in this definition:
• The performance measures you’ve chosen (RMSE? AUC?)
• The framing of the problem (classification? regression?)
• The predictive models you’re using (SVM?)
• The raw data you have selected and prepared (samples? formatting? cleaning?)” Tomasz Malisiewicz

“As many organizations are now learning, it is very difficult to get any value out of large data sets without clear goals, employing sophisticated data science techniques (e.g., machine learning and algorithms) and the right data crunching and analytical technologies. Getting value from data requires special data science methods to differentiate signal from noise to extract meaning and requires special compute systems and power.” Michael Walker ( September 19, 2014 )

“Data is a precious thing and will last longer than the systems themselves.” Tim Berners-Lee

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

“At Mango we define data science as the proactive use of data and advanced analytics to drive better decision making.” Aimée Gott ( 08.06.2018 )

“Data Scientist communities have their own complex jargon; multivariate regression models, Big data engineering, Hadoop, Map Reduce, Deep Learning etc. But, unfortunately businesses do not seem to care about how complex the term is or how impressive the math is! They want the results explained in non-tech terms.” Suman Malekani ( January 29, 2015 )

“All life is an experiment. The more experiments you make, the better.” Ralph Waldo Emerson

“We are currently witnessing a land rush of investment in Big Data architectures promising companies that they can turn their data into gold using the latest in distributed computing and advanced analytical methods. Although there is indeed much potential in applying machine learning and statistical analysis to largedatasets, many companies are hardly sitting on the kind of data that will allow them to compete using hundreds of machines chugging through terabytes of data.
But that’s okay. There is a massive benefit to just getting an organization to understand what data they do have and how they can deploy intelligent models on this data to disrupt their current approaches to doing business. This does not require the latest in parallel computing or bursting into new map-reduce paradigms just to derive insight. It does not require huge data warehouses or terabytes of unstructured data. What it does require is good science on good data. This is where organizations need to start; becoming ”data aware’ and building an organizational culture that understands data as a real asset.” Sean McClure ( November 3, 2014 )