Data Analytics with Managerial Applications internship

2. Data Scientists bring structure to large
quantities of formless data and then analyze
them. They help decision makers shift from
ad hoc analysis to conversing with big data.

3. List the two most
(important/interesting/
informative)
insights from this ARTICLE?

4. Data Science is growing
exponentially. It involves a mixture
of abilities – coding, hacking,
analyzing, communicating and
curiosity.

5. Data Scientists are rapidly getting inducted
into business, trade and industry.
Here, Demand >> Supply

6. Data Scientists want to build things, not just give
advice. Their main attraction to the job is the
prospect of being in developing situations with
real-time awareness. In business, they advice
executives and managers on the implications of
data for products, processes and decisions.

8. Why and How are these
insights relevant to a
manager in India?

9. .
Capitalizing on big data depends on hiring
scarce data scientists – managers must
learn to identify that talent, recruit and make
it productive.

10. A company is a huge enterprise. With no very
formal positions defined as of yet for data
scientists, managers must figure out where can
their skills contribute best and add maximum
value. They must also decide how to measure the
performance of their hired data scientists.

12. They must come up with a way to keep their data
scientists at the top of their game – help them
mingle in their community for practice and
awareness and also help them interact with their
general management colleagues. A careful
balance must be maintained.

14. They must be alert in terms of retaining their data
scientists because of the competition and surge
in their demand, accompanied by scarce supply.
They must come up with the most lucrative terms
of employment, pay perks etc. while making sure
that the company maintains a good margin of
gain.

16. They must make important choices when
deciding how much freedom to give to their data
scientists and decide what probability of
experimentation, hypotheses and uncertainty
they can risk.
Example : the case of Goldman and Hoffman of
LinkedIn.