Analytical Skills
Analytics
involves the ability to determine which data is relevant to the question that
you are hoping to answer, and interpreting the data in order to derive those
answers.
If
you have a knack for spotting patterns, and establishing links between cause
and effect, then these skills will prove invaluable if you’re tasked with
turning a business’s data into actionable plans of operation.
Creativity
There
are no hard and fast rules about what a company should use big data for. It is
an emerging science, which means the ability to come up with new methods of
gathering, interpreting, analysing and – finally – profiting from – a data
strategy, is a very valuable skill.
The
corporate data superstars of the future will be people who can come up with new
methods of applying data analytics in innovative ways. Often they will be
solving problems that companies don’t even know they have – as their insights
highlight bottlenecks or inefficiencies in the production, marketing or
delivery processes. In particular, creativity is important for anyone hoping to
make sense of unstructured data – data which does not fit comfortably into
tables and charts, such as human speech and writing.
Mathematics
and Statistics
Good
old fashioned number crunching. Despite the growing amount of unstructured data
being incorporated into data strategies, much of the information being gathered
and stored, ready for analysis, still takes the form of numbers.
And
even when dealing exclusively with unstructured data, the objective of the
exercise is often to reduce elements of the data – emails, social media
messages etc – to figures which can be quantified, in order for definite
conclusions to be drawn from them. This means candidates with a strong
background in maths or statistics are ideally placed to make the leap into big
data enterprise.
Computer
Science
Computers
are the workhorses behind every big data strategy, and programmers will always
be needed to come up with the algorithms that process data into insights. This
is a very broad category which covers a whole range of subfields, such as
machine learning, databases or cloud computing, which will be great additions
to any budding data scientist’s arsenal. In particular you should be familiar
with the range of open-source technologies – Hadoop, Python, Pig etc. – which
make up the foundations of most big data enterprises.
Business
skills
An
understanding of business objectives, and the underlying processes which drive
profit and business growth are also essential. The idea that a company will
hire an “egg head” data scientist who will be locked away in a basement lab, to
work their magic on data fed to them through a slot in their door, is dangerous
and wrong. They should have a firm grasp of the company’s business goals and
objectives as well as an understanding of the indicators which let them know if
they are heading in the right direction.
Communication
ability
Both
inter-personal and written – an essential part of a data scientist skillset is
the ability to communicate the results of the analysis to other members of
their team as well as to the key decision-makers who need to be able to quickly
understand the key messages and insights.
This
also includes the skills of visualising and reporting data in the most
effective manner. You can have the best analytical skills in the world, but
unless you are able to make your findings understandable to everyone else you
work with, and demonstrate how they will help to improve performance and drive
success, they will be of little use to any business.
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