Five essential skillsets that are required from any data scientist who wants to be competitive in today’s market. Some of them are more valuable to organizations with a need for strategic planning of their data-driven enterprises, and some are more valuable for organizations needing people who are willing to get their hands dirty with the nuts-and-bolts mechanics of data.

1. 5 Essential Skills Any Data
Scientist Needs

2. • The Data Scientist require many of the same
skillsets – but the distribution of your
expertise and experience within these skillsets
will vary, depending on whether your role is
more strategic, or operational.
• So here’s an overview five essential skillsets
that are required from any data scientist who
wants to be competitive in today’s market.

3. Business Skills

4. • You should feel comfortable with the KPIs and metrics that
business strategists use to evaluate every aspect of an
organization, from its stock performance to its human
resources. You also need to be able to evaluate what it is that
makes your business thrive and stand out from the
competitors – and if it doesn’t, you need to have ideas about
how to make it so.
• Also include communication skills under this heading,
although of course they are important across all disciplines.
But particularly in business, the ability to clearly put across
the ideas so that every member of the team knows what you
are doing, why you are doing it, and how you are going to
achieve it, is essential.

5. Analytical Skills

6. • The ability to spot patterns, discern the link between cause and
effect, and build simulated models which can be warped and
woven until they produce the desired results is the domain of
the both the operational and strategic data scientist.
• Once your distributed storage is threatening to spill over with
the reams of structured and unstructured data your machines
have pulled in for you, it’s still going to take a human brain to
make any sort of sense out of it. As such, You’ll need a thorough
understanding of interpreting the reports and visualizations
wringed from your reams of data.
• You will need a grounding in industry-standard analytics
packages such as SAS Analytics and Oracle Data Mining and a
firm idea of how to use them to spot the answers to the
questions you’re asking.

7. Computer Science

8. • Data is of course essential to everything that
computers do, so it’s natural that those with an interest
in programming, networking and system architecture
often gravitate towards analytics and predictive
modeling.
• And it’s a good job, too – as techie types are needed
for everything from plugging together the cables to
creating the sophisticated machine learning and
natural language processing algorithms – or whatever
happens to be pushing the boundaries of what we can
do with the help of our silicon-based assistants today.
• In particular, candidates with a firm grasp of key open
source technologies – Hadoop, Java, Python etc. - are
keenly sought, as these are the foundations of many
organizations’ plans to use data to dominate the world.

9. Statistics & mathematics

10. • A statistician’s skills come into play in just about every
aspect of an organization’s data operations. They will
help to define relevant populations and appropriate
sample sizes at the start of a simulation and to report
the results at the end. Statistics (and its big brother,
maths) is another academic wellspring from which
gushes a torrent of talent into the data science
workforce.
• Whether your role is strategic or operational, a basic
grasp of statistics is essential, but if you veer towards
the operational, a more thorough education in the
subject will be highly desirable.
• Mathematics, too, will come in very useful – despite
the huge increase in the amount of unstructured and
semi-structured data we are analyzing, most of it still
comes out as good old-fashioned numbers.

11. Creativity

12. • Anyone can be formulaic – today, businesses
want innovation that will set them apart from
the pack, both in terms of their corporate results
and the image they present to their consumers.
• The possibilities made available by the
application of data science are constantly
evolving. With the explosion in the number of
organizations realizing the advantages of
leveraging data for insights that will prompt
growth, people able to come up with creative
methods of applying these skillsets will have a
bright future ahead of them.

13. Thank You!
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Source: http://www.datasciencecentral.com/