The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation

3. • If “sexy” means having rare qualities that are
much in demand, data scientists are already
there. They are difficult and expensive to hire
and, given the very competitive market for
their services, difficult to retain.
• There simply aren’t a lot of people with their
combination of scientific background and
computational and analytical skills.
So, what do they mean by the sentence “Sexiest job
of the 21st century”

4. International Data
Corporation (IDC)
predicts a need by
2018 for 181,000
people with deep
analytical skills, and
a requirement five
times that number
for jobs with the
need for data
management and
interpretation skills.

5. So, What is the typical work of a
Data Scientist?
What Data scientists do, is make
discoveries while swimming in
data. It’s their preferred method
of navigating the world around
them. At ease in the digital
realm, they are able to bring
structure to large quantities of
formless data and make
analysis possible.

6. INSIGHTS: “Data Scientist: The
Sexiest Job of the 21st Century by
Thomas H. Davenport and D.J. Patil”

7. 1. Rigorous and
copious efforts are
needed to bunch
the appropriated
people and guide
them through the
market, where,
there is a wild race
going on.
INSIGHT #1

8. • The companies must keep the image
of the scientist in mind—because the
word “data” might easily send a search
for talent down the wrong path.
• Some of the best and brightest data
scientists are PhDs in esoteric fields
like ecology and systems biology.
• George Roumeliotis, the head of a
data science team at Intuit in Silicon
Valley, holds a doctorate in
astrophysics.

9. Would be wise to
wait until that second
generation of data
scientists emerges,
and the candidates
are more numerous,
less expensive, and
easier to vet and
assimilate in a
business setting?

10. If companies sit out this
trend’s early days for lack of
talent, they risk falling behind
as competitors and channel
partners gain nearly
unassailable advantages.
Think of big data as an epic
wave gathering now, starting
to crest. If you want to catch
it, you need people who can
surf.

11. 2. Data scientists
want to build things,
not just give advice.
Data scientists want to be
in the thick of a developing
situation, with real-time
awareness of the evolving
set of choices it presents.
INSIGHT# 2

12. Data scientists don’t do
well on a short leash.
As the story of Jonathan
Goldman illustrates, “Their
greatest opportunity to add
value is not in creating
reports or presentations
for senior executives but in
innovating with customer-
facing products and
processes.”

13. The dominant trait among data
scientists is an intense curiosity—
a desire to go beneath the surface
of a problem, find the questions at
its heart, and distill them into a
very clear set of hypotheses that
can be tested.
Think of him or her as a hybrid of
data hacker, analyst,
communicator, and trusted
adviser. The combination is
extremely powerful—and rare.

14. Employing the Insights into the life of a
Manager.

15. Deductions from Insight #1
The sudden appearance of the Data Scientists on the
business scene reflects the fact that companies are now
wrestling with information that comes in varieties and
volumes never encountered before.
A manager must understand the following statement!!
If the organization stores multiple petabytes of data, if the
information most critical to your business resides in forms
other than rows and columns of numbers, or if answering
your biggest question would involve a “mashup” of
several analytical efforts, you’ve got a big data
opportunity.

16. The company needs to be
very choosy and strict while
hiring a Data Scientist. Hiring
a Quantitative Analyst, or a
Data Management Expert
would not fulfil the purpose.
A quantitative analyst can be
great at analyzing data but not
at subduing a mass of
unstructured data and getting
it into a form in which it can be
analyzed.
Similar, A data management
expert might be great at
generating and organizing data in
structured form but not at turning
unstructured data into structured
data—and also not at actually
analyzing the data.

17. DEDUCTIONS FROM INSIGH
A Data Scientist should by
employed for the work he is
entitled for, not just for
reporting the work and making
the presentations.
If a company could direct the
brain power and the abilities of
a Data Scientist in the right
direction, it could work out
wonders for the company.

18. Examples of the companies, innovating with Data
Science
• LinkedIn isn’t the only company to use data
scientists to generate ideas for products,
features, and value-adding services.
• At Intuit data scientists are asked to develop
insights for small-business customers and
consumers and report to a new senior vice
president of big data, social design, and
marketing. GE is already using data science
to optimize the service contracts and
maintenance intervals for industrial products.

20. Google, of course, uses data
scientists to refine its core
search and ad-serving
algorithms. Zynga uses data
scientists to optimize the
game experience for both
long-term engagement and
revenue.
Netflix created the well-
known Netflix Prize, given to
the data science team that
developed the best way to
improve the company’s
movie recommendation
system. The test-preparation
firm Kaplan uses its data
scientists to uncover
effective learning strategies.

21. • Any Data Driven company should be very
enthusiastic in hiring the appropriate people for
the Data Scientist role.
• Proper Training needs to be provided in the Data
Science field, for which efforts need to be taken
by the universities as well as the corporate
industries.
• Employing the Data Scientist in the work, he/she
is entitled for, is the major thing to be kept in
mind.