We’ll go over the level of 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 Data science.
Level Of Education
Our results show that virtually all Data scientists have graduated from an institution of higher Education. This includes
However, some Degrees seem to be much more popular than others. In fact, only around 3% 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, bachelor’s, master’s, and PhDs round up roughly 91% of the Data, with 79% being split among masters and PhDs.
This means that roughly four out of every five Data scientists have at least a master’s Degree. So yes, going for a graduate program is highly recommended.
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 less BA or MS Degrees as the desired Educational level.
So it’s safe to say that a PhD is not a requirement for This job. But an added bonus. Well, that’s partly because the vast majority of the PhDs have a lifelong interest in doing research. So they’re harder to lure away with some lucrative job ads.
All right, Another factor that plays a role is also the amount of time a candidate has already spent in Data science or related field. On average, employers expect about three and a half years of experience in the field for an undergraduate, compared to only two and a half for somebody with a graduate Degree.
Therefore,having an MS compared to a BA 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 BA 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 scientist. So let’s move on to the reason you’re all here,
The best disciplines, and major a concentration or a discipline no matter how you call it, each Degree has a field of expertise.
Our research suggests that 89% of Data scientists come from a quantitative background, whether it’s the BA or the MS, 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 in second because they help students develop an analytical way of thinking.
Over the last three years, we see a definitive trend that computer science is the most well represented non Data science 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 and 2020 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 is 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. Another important group in our cohort is economics in social sciences.
This may seem rather odd at first, but people who graduate these disciplines can simultaneously analyze 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 business or financial mindset is always welcome. What about Data science as a Degree
in 2019, Data science as the Degree itself was not really that hot. With a mere 12% of current Data scientists owning a concentration in the field, things are changing, but slowly so today, 21% of current Data scientists graduated from a Data science Degree.
And while this is the top group in our research, it is evident that it is not very predominant. The main reason was that DNS was still very new as a discipline, and was 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.
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 adds 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.
If you have already graduated in a certain field, you’re transferring to Data science, maybe already predetermined.
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.
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 as how well renowned the institution you got it from is. Our research 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 one in every three Data scientists graduated from One of these 50 institutions. In comparison 9%, or one 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 one in 10 Data scientists holds a Degree from a school ranked between 100 and first and 200th place.
This trickling down might not sound very shocking, but consider the following 100 universities make up 9% of the sample, whilst 50 make up 31%.
This means that you are about six 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 1000, 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 went 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 straightforward there.
All right. 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.