2018-2019 exchange Winter 2019 Newsletter FINAL | Page 13

Glenn: Very true. You can’t really do any good data science without
understanding the context.
Jen: Right, the practical application in business.
Glenn: The specific things that are in the popular press are things
like credit scores. Credit scores are an application of data science,
which is taking a lot of information—in this case credit data that is
available on people— and predicting whether they are going to de-
fault on loans in the future. Everybody knows that a credit score is an
important number because it drives interest rates and loan approvals.
Credit scores are outcomes of data science.
Other things in the press these days are self-driving cars and
Amazon’s Alexa, which is basically speech recognition and an-
swer formulation. Those are various forms of data science. In
the life insurance business, it deals with things like underwriting mod-
els, which can predict somebody’s insurance risk. Or it could be mar-
keting predictions, like who is likely to respond to an offer for insur-
ance or who is likely to be retained as a customer. It could be cus-
tomer segmentation; what types of customers do we have and how
do we treat them.
Jen: Let’s talk about the type of education or skill set that is needed
to be a data scientist.
Glenn: There is no single path to becoming a data scientist anymore.
It used to be that statistics or computer science could be the entry
point. Those are still good foundations. Nowadays, people also may
have any technical degree or get involved in data science by learning
on the job and analyzing a lot of data. Or they could come from psy-
chology or economics and do a lot of data analysis from that side.
There are also many online courses available on different data sci-
ence methodologies and tools. But the skill set that people ultimately
need to achieve is certainly statistical modeling— understanding the
concepts and nuances for statistical modeling— and mastering a few
different coding languages. On the tool side, the most common tools
in data science are SAS, R and Python, and they are very flexible.
Point and click tools also exist but they are less powerful.
Jen: How would you define data analytics?
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