Citizen Data Scientist

Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense

Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense

No one would say “Citizen Lawyer” or “Citizen Nuclear Physicists” or “Citizen Physician.” I guess a “Citizen Physician” would be someone who “practices medicine but whose primary job function is outside of the field of medicine (meaning that they’ve had no training in medicine or medical procedures).”

Okay, let me get this out there: I find the term “Citizen Data Scientist” confusing. Gartner defines a “citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”

While we teach business users to “think like a data scientist” in their ability to identify those variables and metrics that might be better predictors of performance, I do not expect that the business stakeholders are going to be able to create and generate analytic models. I do not believe, nor do I expect, that the business stakeholders are going to be proficient enough with tools like SAS or R or Python or Mahout or MADlib to 1) create or generate the models, and then 2) be proficient enough to be able to interpret the t-tests, f-scores, p-values and residuals necessary to ascertain the analytic model’s goodness of time.

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No one would say “Citizen Lawyer” or “Citizen Nuclear Physicists” or “Citizen Physician.” I guess a “Citizen Physician” would be someone who “practices medicine but whose primary job function is outside of the field of medicine (meaning that they’ve had no training in medicine or medical procedures).” They call those people quacks (not quants... he-he-he).

WebMD doesn’t make someone a doctor any more than analytics makes someone a data scientist. Analysis of the analytic results and insights is an important step in the process, particularly when the results contradict each other. Data scientists provide the necessary experience about the different analytic techniques and algorithms required to decipher the results, validate the results and then turn the results into actions or recommendations.

What’s wrong with the definition is that it doesn’t properly acknowledge the deep training in analytic disciplines such as machine learning, cognitive computing, data mining, computer programming, and applied mathematics. It also dismisses the critical importance of gaining hands-on, data science experience through years of apprenticeships and tutelage under the guidance of master data scientists.

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In order to understand the importance of the role of the data scientist, I solicited the help of the best data scientist that I know... Wei Lin. Wei and I have done numerous big data projects together and every time I engage with Wei, I learn tons. So naturally, I’d call upon a true master data scientist to help me write this blog.

  The starting point for the data scientist discussion starts with an understanding of the types of tasks at which a data scientist must become proficient.

 



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