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Thursday, February 14, 2019

Doing Pre Data Science

Nicely put piece,  that I have passed it along before to others,  worth considering again.   Especially good as it includes the business parts more explicitly than you typically.   Prework is a good idea.  But also make sure you have the right team doing the work, especially the business stakeholders, and it should be pre, during and followup work that gets the same attention.

How Do You Win the Data Science Wars?  You Cheat By Doing The Necessary Pre-work!
Posted by Bill Schmarzo  in DSC

I’m sure that most data scientists have experienced that moment when they realize that the folks around them have no idea what they do.  That moment when someone walks up to them and says “I’ve got some data.  Can you do some data science on it?”

Many organizations started their data science journey by hiring a “data scientist” and asking him or her to perform magic on the data.  And while there are countless problems with that approach, companies quickly learned that 1) not everyone who calls themselves a data scientist is a data scientist (I can call myself young and dashing, but that don’t make it so) and 2) there is no magic when it comes to data science. Sorry, but as Chris Rock famously said:

There is no sex in the champagne room…

Data Science is very hard work, requiring experience and expertise in gathering (scraping in some cases) data from a wide variety of poorly documented and hard-to-access data sources; dealing with the incompleteness, inaccuracies, vagueness and poor documentation about the data; massaging, twisting and torturing that data into some useful form; and trying a seemingly endless number of analytic transformations, enrichments and algorithms in an attempt to find those combinations of variables and metrics that might yield a better predictor of performance (see Figure 1).  ... "

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