The ‘Holy Trinity’ of Data Science

There are probably dozens
of variants of the Venn diagram that Drew Conway proposed a few years ago
to capture the core skills of a data scientist. Needless to say, the role has experienced
many changes since then, while rapid technological developments and the boom of
AI have further propelled the profession to the top of LinkedIn’s emerging
jobs ranking.

Well — we couldn’t resist putting forward
our own version of the infamous Venn diagram. Like Conway’s, ours is built on three
axes. However, our model focuses on broader categories rather than on specific expertise.
In today’s ever-changing business world, soft and cross-cutting skills are the
truly decisive factors that, in the long run, can ensure adaptability and
success.  

Thus, our “holy trinity,” if you will, of
data science is made up of:

• Curiosity
• Technical know-how
• Collaboration

Thinking of a career in the field, or
wondering if you’re doing this right? Let’s dive into each component.

The importance of a curious mind

Probably obvious, but it’s impossible to
talk about science and not mention the innate curiosity that powers it. Whether
you plan to explore the possibility of life in other planets or the mysteries
of quantum entanglement, it is the thirst for answers to questions and riddles
that will make you advance.

This, of course, applies to the problem-solving
capabilities required in data science projects. Nevertheless, well-directed technical
inquiries tend to fall on shaky ground whenever there are not accompanied by a
good contextual understanding. Just because you’re good at playing with data and
creating models that produce intricate insights and machine learning
experiences, none of it is worth anything if your work isn’t helpful to the
overarching goal.

For this reason, the need for curiosity
expands to the domain of expertise in which you operate (i.e. finance, political
studies, marketing). The more you know about the field of work of your company
or department, the better questions you will ask yourself, the useful insights
and models you will produce.

Note that we’re highlighting “curiosity” rather than “knowledge.” You’re going to spend many hours working with this data. Make sure it’s something that you are passionate about or at least find interesting.  

Knowing the technical ins and outs

Some describe a data scientist as someone
who knows more about math and statistics than your average programmer while
having greater coding capabilities than your average mathematician. Although this
definition errs on side of oversimplification, it is not totally misguided.

To be successful in data science, you need
to be proficient in certain data engineering and coding-related methodologies
and practices. It is important not only to know how to build effective code, but
also how to efficiently extract and clean data.

Additionally, there is the crucial
technical knowledge that has less to do with computer engineering and more with,
for instance, data privacy compliance. You must know what data sets you can
manipulate and which ones you can’t, which processes can be computed on the
cloud and which ones are better reserved for on-premises infrastructure. At the
same time, if you work in finance or in any other field where sector-specific concepts
are a basic requirement, you will have to dominate those on top of your
knowledge of data science.

Playing as a team

This is where soft skills play the biggest
role. Interpersonal communication and teamwork have always been one of the key
factors of success Their relevance in this hyperconnected world of ours is only
increasing.

There must be good cooperation between all
teams and stakeholders involved in the process, and, for that, you should be
able to communicate efficiently and in a compelling way. It’s not enough with working
closely with developers or analysts. Knowing how to present a project in
layman’s terms becomes essential if you want to be granted the staff or
computational power that you’ll need to complete it.

Apart from this, you need to be well-versed
in concepts like Agile development, which help teams streamline the production
pipeline. Version control, a unified repository, and a good understanding
between development and production are a teamwork-must in today’s IT world.