Hi and welcome to our new 365 Data Science
special!
Today, to get into Data Science, you need
a degree that signals potential employers
you are the qualified candidate they’re
looking for. We here at 3-6-5 Data Science
have conducted several studies on this topic
to determine what are the best degrees for
an aspiring Data Scientist. So, in this video,
we’ll go over the level, discipline and
university rank you should be looking at when
deciding what degree is worth pursuing or
if your current degree is suitable for the
field.
But before we get down to the results, we
want to quickly disclose the methodology behind
our approach. For the third consecutive year,
we’ve used LinkedIn to gather background
information of current data scientists. We’ve
used their education and prior experience
to help us identify the credentials required
to enter the field. What’s more, we’ve
collected data from job-search websites to
determine the most important qualifications
and skills employers are searching for in
a data scientist.
Let’s start with the level of education.
Our results show that virtually all data scientists
have graduated from an institution of higher
education. This includes Bachelors, Masters,
MBAs, and Ph.Ds. However, some degrees seem
to be much more popular than others.
In fact, only around 2% of all data scientists
in our sample owned an MBA, but that’s not
entirely surprising. If you decide to do an
MBA, chances are you’re not aiming at the
hands-on technical data scientist role on
the team.
Bachelors, Masters, and Ph.Ds round up roughly
95% of the data, with 75% being split among
Masters and PhDs. This means that roughly
3 out of every 4 data scientists have at least
a master’s degree. So, yes, going for a
graduate program is highly recommended.
Of course, if you think a B.A. is as high
as you want to go, there is no need to be
discouraged. Nearly 20% of the data scientists
in our sample had only completed an undergraduate
prior to entering the field. And while this
number is not high, the percentage of data
scientists holding only a Bachelor’s degree
has been steadily growing over the last three
years.
This is a refreshing indicator that shows
employers are starting to value skills over
years of schooling. In other words, a qualified
candidate today has a higher chance of breaking
into the field, compared to two years ago.
And if we take a quick look at the job adverts
available online, we’ll see that most of
them list B.A. or M.S. degrees as the desired
educational level. So, it’s safe to say
that a Ph.D. is not a requirement for the
job, but an added bonus. Well, that’s partly
because a vast majority of the PhDs have a
lifelong interest in doing research, so they’re
harder to lure away with some lucrative job
ads.
Alright.
Another factor that plays a role is also the
amount of time a candidate has already spent
in data science or a related field. On average,
employers expect about 3 and a half years
of experience in the field for an undergraduate,
compared to only 2 and a half for somebody
with a graduate degree.
Therefore, having an M.S. compared to a B.A.
roughly equates to a year’s difference in
the field. Of course, this comes as a result
of the proficiency graduate students are expected
to have, compared to undergraduates. All things
considered, it’s quicker to break into Data
Science if you’ve got a Master’s degree,
so that’s probably the safer route to success.
However, it must be noted that it’s also
the more expensive approach.
That said, what you want to do after graduation
plays a big role as well. For example, if
you plan on breaking into Consulting, you’ll
definitely need a graduate degree. But if
you want to succeed in data-driven recruitment,
a B-A will work just fine. Different job roles
and activities require different degrees,
so you should take this into account when
making a choice.
Okay! We’ve discussed the level of education
best-fitting for a Data Scientists, so let’s
move on to the reason you’re all here: the
best disciplines.
A major, a concentration or a discipline – no
matter how you call it, each degree has a
field of expertise. Our research suggests
that 91% of data scientists come from a quantitative
background. Whether it’s the B. A., or the
M. S., usually at least one of the degrees
is quantitative.
Of course, natural sciences and math-heavy
social studies degrees are considered quantitative
as well. The first, because they require conducting
experiments and extracting insights, and the
second – because they help students develop
an analytical way of thinking.
Over the last 3 years, we see a definitive
trend that, with 22%, Computer Science is
the most well-represented degree among data
scientists. Of course, this isn’t a complete
shock, since good programming skills are essential
for a successful career in the field.
Similarly, it’s not all that surprising
that a degree in Statistics or Maths is among
the top of the list as well. After all, the
ability to correctly interpret the results
is a huge part of Data Science. However, the
16% recorded in 2019 mark a decrease from
previous years. The main reason behind this
decline comes from the ongoing rebranding
of the discipline. What was once known as
Statistics is being intertwined with other
majors and presented as Business Statistics,
Econometrics or even Machine Learning. Thus,
Statistics’ share of the pie is slowly being
split among the other fields, which are benefiting
from this name change.
With a decrease in the stats representation
comes an increase in another group – economics
and the social sciences. This may seem rather
odd at first, but this is the second most-represented
degree choice among data scientists. Why?
Because people who graduate these disciplines
can simultaneously analyse the data properly
and build a story around the insights they
find. Yep, simply stating a change in X resulted
in a change in Y is often not good enough.
We also need to construct sets of rules to
take advantage of this knowledge.
Another reason for the influx of economics
majors is that many of them start off as analysts
and gain valuable knowledge and experience
in the field as they go. Overall, the analyst
role has become a catalyst for many social
studies graduates who want to transition into
data science eventually.
In addition, a lot of the work in data science
is related to optimizing financial decisions
and policies, so a business or financial mindset
is always welcome.
What about data science as a degree?
Data science as a degree itself is not really
that hot, with a mere 12% of current data
scientists owning a concentration in the field.
The main reason is that D.S. is still very
new as a discipline and is not that widely
offered in universities across the globe.
The limited availability leads many students
to pick one of the other related options,
like computer science or statistics. So, the
most obvious choice, isn’t particularly
the correct one, when it comes to picking
a degree.
Of course, the trend might shift within the
next decade, but for now – data science
as a degree is still playing catch up to the
more popular options.
Now, if we have a look towards the current
job market, we’ll see some slightly different
trends. Checking the most-commonly sought-after
concentrations in the field, one sees Math
and Statistics as the clear leader. This is
especially true for companies looking for
graduate-level employees. In those cases,
roughly 86% of all Data Science ads listed
Mathematics, Statistics, or both among the
desired concentrations for the job.
The shift in the trend comes from Consulting
firms not looking for Computer Science majors.
This may come as a shock, but under 30% of
those firms listed Computer Science as the
desired concentration for potential candidates.
Of course, that can be attributed once again
to the preference for great storytellers,
high demand for understanding data analytics
and economics, and maybe a bit of a prejudice
against CS graduates.
So, we see that, in general, computer science
is the leader among current data scientists,
but stats and mathematics are what employers
are looking for at the moment. Of course,
this can also be attributed to the emergence
of high-level languages such as Python and
R.
Either way, it is known that different aspects
of data science desire candidates from specific
fields. Therefore, knowing exactly which domain
of data science you want to make a career
into should play a crucial role in your choice
of discipline. And vice versa – if you have
already graduated in a certain field, your
transfer into data science may be already
predetermined.
But here’s the thing - many up-and-coming
students apply for college and university
without having a fixed career path in mind
and that’s an issue we’ve been trying
to tackle for several years now. We’ve created
‘The 365 Data Science Program’ to help
people enter the field of data science, regardless
of their background. We have trained more
than 350,000 people around the world and are
committed to continue doing so. Apart from
basic training, we offer Portfolio Advice
and Resume feedback to help you achieve your
goals. If you are interested to learn more,
you can find a link in the description that
will also give you 20% off all plans if you’re
looking to start learning from an all-around
data science training.
Okay.
We still have one more important aspect we
haven’t discussed - the rank of the university
you’re considering.
Even though your major is important, so is
how well-renowned the institution you got
it from is. Our researched showed that roughly
31% of current data scientists hold a degree
from one of the top 50 universities listed
by Forbes magazine. This is really significant
because it essentially states that roughly
1 in every 3 data scientists graduated from
one of these 50 institutions.
In comparison, 9%, or 1 in every 11, graduated
from a university outside the top 50, but
inside the top 100 in the rankings. Going
further down the rankings, we see that 1 in
10 data scientists holds a degree from a school
ranked between 101st and 200th place. This
trickling down might not sound very shocking
but consider the following: 100 universities
make up 10% of the sample, whilst 50 make
up 31%. This means that you are about 6 times
more likely to become a data scientist if
you went to a high-ranking school.
Moreover, if we add these numbers together,
we see that the top 200 schools are responsible
for producing 50% of all data scientists in
the field. So, having a degree from an elite
institution is a bigger signal to employers
that you are a worthy candidate than what
discipline you majored in.
However, don’t be quick to despair - there
is a silver lining.
Around one-fourth of all data scientists within
our sample either have a degree from a school
ranked outside the top 1,000 or one not even
present in the rankings. That suggests that
sufficient experience and skills can actually
outweigh a university degree!
That said, if you can’t get into an elite
institution, make sure to sharpen your coding
and statistics skills enough to stand out!
So, what conclusion can we arrive at?
Well, to summarize, a graduate degree from
a prestigious school is your best bet of becoming
a data scientist. However, the best concentration
varies, depending on what you want to work
afterwards. Computer Science is the safest
option, as it gives you a lot of freedom and
is highly sought-after. But if you intend
to go into Consulting, Math or Statistics
are a better choice. Alternatively, if you
plan on becoming a data analyst first, you
can look for a degree in Economics, since
the progression-line is much more straight-forward
there.
Alright. Now you know how to start your journey
into data science.
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