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.
