For the last 3 years we at 365 Data Science
have been trying to answer one big question:
“What makes a data scientist?”
Since we are talking data science, the only
logical way to approach the question is to
ask the data. And that’s what we’ve done
for 3 consecutive years. Since 2018 we have
explored 1001 data scientist LinkedIn profiles
to uncover the most interesting trends in
the data science field.
In this video we will go through the most
important findings from the last 3 years.
In fact, we have created a very cool and interactive
PowerBI dashboard which you can use to analyze
the data yourself. Link in the description.
If you prefer to digest our own analysis just
carry on with the video.
According to the data, the average data scientist
from 2018 to 2020 is a male with a second-tier
degree, coming from a quantitative background,
which is not necessarily data science or computer
science. Their preferred programming language
is Python, but they’d often know R and SQL.
Many of the new data scientist positions are
being filled by people who are already data
scientists, so the field feels much more saturated.
Getting into data science still looks like
a great opportunity, but the ‘data scientist’
position becomes more and more exclusive.
For sure, the current COVID-19 pandemic would
have a say on the demand for data science
professionals, too. However, this is maybe
the right time for you to dedicate yourself
to starting a career in data science, once
the pandemic is over.
And if you’ve set your sight on a career
in data science – we’ve got you covered.
We developed the ‘3-6-5 Data Science Program’
to help people of all backgrounds enter the
field of data science. We have trained more
than 450,000 people around the world and are
committed to continue doing so. If you are
interested to learn more, you can find a link
in the description that will also give you
a special offer on all of our plans.
That said, let’s dive deeper into the education,
years of experience and programming skills
of a data scientist from 2018 to 2020.
Education
Our sample shows that at least 80% of the
people held a minimum of a Master’s degree.
This isn’t as surprising, considering data
science is a field that expects advanced know-how
from the person — usually achieved by graduate
or postgraduate types of education, or independent
advanced research in other cases.
And while specialization is important, a Ph.D.
is not really a requirement for breaking into
data science. Indeed, over the years, the
number of PhD holders has remained consistent,
making up about 27 per cent of our study.
On the contrary, starting from 2018 there
was a rise of about 20% in the professionals
with a Master’s degree compared to the 2019
cohort.
Area of Studies
In 2018 and 2019, “Economics and Social
Sciences”, “Computer Science” and “Statistics
and Mathematics” were filling up the top
3 most popular fields of study of data scientists.
2020 was the first year ever that featured
“Data Science and Analysis” (22%) as the
top degree. Therefore, we can assume that
universities have started to catch up with
the demand for data science education.
Graduates form the Engineering, Natural Sciences,
and Other fields constitute approximately
11% of our data each. This indicator has remained
stable throughout the years.
Interestingly, in 2020, most women in our
sample have earned a ‘Statistics and Mathematics’
related degree (24% of the female cohort).
In comparison, men most likely earned a degree
in Data Science and Analysis (22%), with Computer
Science (19%) being a close second.
Years on the job
If you are changing jobs, or working through
your data analyst years, you must be wondering
whether you’ve got the right experience
for the position. In terms of tenure, in 2018
almost all data scientists were ‘newcomers’
to the ‘data scientist’ position. Some
of this was driven by name changes to their
occupation, but mostly – the supply was
so little that it was way easier to enter
the field.
Currently, we are observing a much tougher
playing field. The majority of data scientists
have more than 2 years on the job and it seems
like a very small proportion of the total
data scientist pool is new. In fact, in 2020,
52% of the cohort, held the title ‘data
scientist’ at their previous position.
Programming languages
The programming skills a data scientist needs
are arguably the most interesting area of
research (at least for us). For many years,
R was the preferred language a data scientist
was expected to “speak”. In 2018 and 2019
Python started ‘eating away at R’. And
it did so at a very fast pace. In 2020, we
have reached the point where Python is by
far the preferred programming language in
the data science community with 74% adoption!
R is not completely overthrown but becomes
less and less favored among professionals.
An interesting development is the rapid year-to-year
growth of SQL users. In 2020, more than 50%
of data scientists actively use the language.
One common assumption is that companies expect
from a data scientist to solve all their data
related problems, no matter if they are related
to data engineering or data architecture.
On another note, the adoption of BI software
such as PowerBI and Tableau has also demanded
a higher understanding of databases. Inevitably,
SQL had to be added to the data scientist
toolbelt for the sake of ‘getting the job
done’.
Alright.
So, looking at the data, the answer to “What
makes a data scientist?” becomes clearer.
Professionals are paving the way and universities
are starting to provide a more tailored education.
From a career point of view, it seems that
it is getting harder to become a data scientist
as data scientists tend to stay on their job
for a longer period of time. However, different
opportunities to get into the field remain,
as demand still varies across countries and
industries.
One thing is for sure – learn Python, if
you are to become a data scientist!
And if you’d like to become an expert in
all things data science, subscribe to our
channel.
Thanks for watching and good luck!
