So here’s an overview of what I feel are the 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.
However with a broad understanding of all of
them, as well as an idea as to where your own particular strengths and
interests may lie, you’re in a strong position to sell yourself to the
growing number of companies looking to hire top-notch data talent.
Business Skills
Particularly for those whose
calling lies towards the “strategic” end of the spectrum, a thorough
understanding of what keeps businesses ticking – and more importantly,
what causes them to grow – is an essential field of expertise.
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.
I 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.
Analytical Skills
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, IBM
Predictive Analytics and Oracle Data Mining and a firm idea of how to
use them to spot the answers to the questions you’re asking.
Computer Science
If an eager, fresh-faced
graduate from college or high school has had any exposure to the world
of data science before throwing themselves into the workforce, it will
probably have been in the computer science lab.
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.
Statistics/ Mathematics
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.
Creativity
In this sense, creativity is the
ability to apply the technical skillsets mentioned above, and use it to
produce something of worth (such as an insight), in a way other than
following a pre-determined formula.
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.
I hope you found this post useful. I
am always keen to hear your views on the topic and invite you to comment
with any thoughts you might have.
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