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Feb 24, 2015

Imparting Data Sciences - Industry Practices - Part 1

This is what happening in the industry from the observation over a period of time;  fresher’s/juniors’ forced to become more proficient with either a programming language or Statistics/ML theory, which is leading to these guys “trying all possible models with the hope that at least one will fit the data well”. This generally can also lead to misuse of the model or algorithm, wherein, here, domain knowledge which is critical and helps them in (as listed below) are ignored completed while imparting the data sciences:

q  Rightly define or appropriately refine the business problem e.g. economic reasoning between price and sales help in accurately determine the model or model tasks for analysis.
q  Can guide and provide right direction as an ad hoc model, may be difficult to justify both analysis and potentiality of the results obtained.
q  More importantly, can provide past knowledge and reduces some trial and error involved in choosing the "right" model for empirical analysis that provide actionable analytical insights.


Author undertook several programs towards data sciences talent development, views expressed here are from his industry experience and personal observations. He can be reached at mavuluri.pradeep@gmail or pmavuluri@analyticaltis.com for more details.

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