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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