Best Data Science Degrees to Get Hired in 2020

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. If you liked this video, don’t forget to
hit the “like” or “share” button! And if you’d like to become an expert in
all things data science, subscribe to our channel for more videos like this one.
Thanks for watching!


  1. ▶ Learn more about our courses and get ** 20% OFF** all plans! ✅
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  2. so im subscribed to their newsfeed also and yesterrday they posted an article that outlines EVERYTHING about the trends in data science, not just education, and highglights key differences by countries — if anyone's interested, this is the article –>

  3. Great video! As a data scientist, I couldn't agree more with the thoughts related to advanced degrees. I have two masters degrees and I am neck deep in student debt. This is definitely something you should consider before going this route. I have some more thoughts on the data science career path on my channel for those who are interested!

  4. am completing my economics and mathematics degree in social sciences department and i want to go for the data science route. can i really make it?

  5. So right now I am doing bachelor in technology with computer science stream in India, and I have been learning about data science, machine learning and deep learning for almost a year and now I am preparing for ms in data science in usa.
    So my question is how can i know or decide about different streams/paths within data science like it is being used everywhere from Healthcare to fintech, how can i know about these paths and then decide as right now I love to both develop machine learning models as well as play with datasets while trying to understand it.

  6. How about an MBA plus bachelor's degree in computer science and engineering? As an MBA will help with the analytical part and computer science will help with the coding part.

  7. How about existing tech professionals considering a move into Data Science? How valuable are a
    strong programming background and SQL skills?

  8. I'm an electrical engineer by education. How would the career track be for me if i want to go in this field?

  9. I have a masters in Analytical Chemistry and want to transit into Data Science field. What could you suggest will work for me? Thanks in advance.

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