Really interesting your point of view. I agree with you. :)
I’d like to endorse the title "data scientist" for professionals (and data science as a scientific area) since it defines a researcher who gather knowledge from Computer Science and Statistics (Math too, of course) to solve problems portrait in data. As all kind of science branches, a data scientist (researcher) has the challenge of developing new approaches, methods, techniques, algorithms for extracting, modeling and/or understanding new phenomena that are implicit in (different) data sources (big volumes in most cases) - as well as just adequately modeling an explicit or previously identified one.
I've been working with Data Science in Agriculture and, as I see, challenges with data go beyond the current knowledge of agronomic, phytopathology or even environmental sciences. Sometimes, agronomists has poor or no explanations for complex phenomena. By analyzing the data, I can see beyond, as well as proposing new hypotheses and experiments for corroborating them, which allow extending the knowledge frontier. Yes, we do science! We make scientific discoveries that would lie dormant in mountains of data.
I like to say that a data scientist is capable of speeding up the "science as whole". The explanation for that: we are researchers with some capacity of extracting complex pattern from data. Complexities in data are our research targets. In this sense, there is no tautology with other science procedures, especially in a digital world where producing data is infinitely easier than extracting useful information and knowledge.
In fact, different companies are in different stages of their data-driven plans. The most part of them actually need to "find that data engineering is the investment they need in order to get the data ball rolling" (like you've said clearly).
Perhaps what we need to do is guiding the market, industry, and recruiters to the role of a "data scientist" as well as the value of science aggregation in the perenniality and sustainability of the agile innovation chain. Perhaps what we need is making clear what Jeff Leek stated in 2013: "The key word in “Data Science” is not Data, it is Science".
Really interesting your point of view. I agree with you. :)
I’d like to endorse the title "data scientist" for professionals (and data science as a scientific area) since it defines a researcher who gather knowledge from Computer Science and Statistics (Math too, of course) to solve problems portrait in data. As all kind of science branches, a data scientist (researcher) has the challenge of developing new approaches, methods, techniques, algorithms for extracting, modeling and/or understanding new phenomena that are implicit in (different) data sources (big volumes in most cases) - as well as just adequately modeling an explicit or previously identified one.
I've been working with Data Science in Agriculture and, as I see, challenges with data go beyond the current knowledge of agronomic, phytopathology or even environmental sciences. Sometimes, agronomists has poor or no explanations for complex phenomena. By analyzing the data, I can see beyond, as well as proposing new hypotheses and experiments for corroborating them, which allow extending the knowledge frontier. Yes, we do science! We make scientific discoveries that would lie dormant in mountains of data.
I like to say that a data scientist is capable of speeding up the "science as whole". The explanation for that: we are researchers with some capacity of extracting complex pattern from data. Complexities in data are our research targets. In this sense, there is no tautology with other science procedures, especially in a digital world where producing data is infinitely easier than extracting useful information and knowledge.
In fact, different companies are in different stages of their data-driven plans. The most part of them actually need to "find that data engineering is the investment they need in order to get the data ball rolling" (like you've said clearly).
Perhaps what we need to do is guiding the market, industry, and recruiters to the role of a "data scientist" as well as the value of science aggregation in the perenniality and sustainability of the agile innovation chain. Perhaps what we need is making clear what Jeff Leek stated in 2013: "The key word in “Data Science” is not Data, it is Science".
Thanks for sharing your excellent text with us.
Ednaldo J. Ferreira