Before we begin, we want to say that throughout this video will be using the abbreviations D S and C s to indicate data science and computer science, respectively.

Additionally, both D S and C s are fantastic choices for a concentration, so please don't feel discouraged if you've already chosen one over the other.

That being said, we hear a 365 have conducted research to determine which one is better for a successful career As a data scientist, we'll begin by weighing the pros and cons of earning either degree, starting with D s.

Then we'll do some evaluation and head to head comparison before picking a winner.

Okay, The most obvious pro of getting a data science degree is that it's supposedly the shortest route to becoming a data scientist.

Having the words data science next to education and your resume is a surefire way to get the express treatment when applying for jobs in the field.

Thus, the signaling aspect of such a degree is extremely important.

Of course, the main reason is that potential employers believe you have a great interest in the job.

They don't have to worry about programming skills, analytical understanding of statistical results or your data storytelling abilities.

This is crucial because some great statisticians lack the coding pedigree.

While some programming wonder kids lack the knowledge to extract insights from a data set with the data science degree, you're sure to possess all the necessary qualities without needing outside validation like extra certification.

However, currently there is one Major Khan when it comes to a data science degree availability.

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If you're looking to start learning from an all around data science training Since the field is relatively new, a data science program can sometimes be hard to come by, regardless of whether we're talking undergraduate or graduate programs.

The scarcity has resulted in many students having to pick alternative concentrations and, as a result, losing interest in the field.

Prior to graduating in one of our other YouTube specials, we mentioned that roughly 50% of all data scientists graduated from some of the 200 top rank universities, according to Forbes.

Still, a very large portion of those do not offer neither graduate nor undergraduate programs in the field.

Add to that the fact that 20.8% of all current data scientist possess a degree in the field, and we come to the conclusion that university rank is a bigger signal than the discipline itself.

Therefore, you might be better off earning a different degree from an elite school than a data science one from a less prestigious institution.

Just remember that the degree has to be quantitative.

As virtually all data scientists come from such a background.

However, there is a downside that, as it seems, you might have to acquire additional credentials before you can break into the field.

Enter these self created data science degree.

Now in many places across the United States and less so in Europe and Asia, it is possible the change your concentration to an interdisciplinary major.

These are the so called self created majors, which incorporate various courses across several departments.

Since data science is a nice mix of analysis, coding and data storytelling, we can combine math or statistics with computer science, and some social science like economics courses across these three departments should be enough to construct a great data science degree without having to learn what you won't need.

What we mean is that by removing pure mathematics out of math, the developing aspects of CS and the policy based aspects of economics, what you're left with are the top skills you need to succeed in the field.

But before you decide to try this out, you need to know that you can't just select random courses to construct such a major.

A much more realistic approach is to find a decent university, which offers such a program, then provide enough evidence showing that some of the available courses in your institution are equivalent to those in their D s program.

That way your degree has the added credibility of the more renowned institution, which is a fantastic bonus.

However, even this option isn't available everywhere, so you might have to concede and go a different route.

If that's the case, you shouldn't feel discouraged since a D s degree is no bed of roses.

One reason for this is how knee shit is To go for a D s degree means you have to be absolutely certain that you wish to become a data scientist.

Other related fields like statistics or computer science provide greater flexibility in terms of professional development.

With those, you can alter your trajectory if data science ends up being different from what you imagined without having to re specialize in acquire new skills.

In comparison, a computer scientist can easily transition into a data architect or data engineer job with the skill set they already have.

We can say a change of heart is a nonissue for them, so computer science is the preferred degree in that regard.

However, let's not get ahead of ourselves and compare the two before we've had the chance to examine the pros and cons of a C s degree.

For starters, a computer science concentration is very reputable and widespread.

Employers have a general understanding of what a C s degree consists of and what to expect from somebody holding one.

Therefore, recruiters look favorably upon graduates who majored in computer science.

The not so obvious bonus year comes in terms of landing internships.

The more popular and accepted the discipline, the higher the chances that you'll manage to intern while still at university.

Since employers tend to value experience and skills over credentials, having an internship can go a long way in making you a good candidate.

However, the downside of this is the over abundance of CS degrees in the job market.

This means that competition in the field is vast and having a computer science concentration is less of a signal today that it was a few years ago.

Computer science brings great flexibility when it comes to programming languages.

If you know one, you know them all because they're fundamentals work pretty much the same way, hence, by knowing java or C plus, plus transitioning to python should be a walk in the park.

Furthermore, CS degrees also includes some discreet math so a computer science major really hits a lot of the data science buttons, However, just like D.

S, C s has its cons.

For one, somebody who has his heart set on becoming a data scientist will also have to learn some coating.

That aspect of computer science is unavoidable.

Since every CS program make sure to give its students the fundamentals less code savvy students might find some coding tasks too difficult or completely outside the scope of their interests.

However, to finish the degree they'll have to master that part of Si ESAs well.

Additionally, many CS students develop a discreet way of thinking, too.

Elaborate algorithms often work on the basis that the result of one action always leads to another.

What's important is not what action follows, but that we always know what is next.

However, that isn't always the case in data science.

Oftentimes, one action means that we have, say, a 60% chance of event, a occurring and a 40% chance of event be occurring.

Thus, not developing a probabilistic mindset is a challenge for CS graduates as they really want everything to be 100% certain all the time.

Now that we've discussed the pros and cons of each degree, let's compare and contrast.

We see that C S is more widespread, so the limited availability isn't a concern.

Second, since it is more well known, your chances of landing an internship are bigger and that will surely enable you to get some experience under your belt.

In addition, virtually all elite institutions offer siestas a concentration, and 50% of all data scientists come from schools ranked in the top 200.

Therefore, going for A C s degree in the high ranked college will greatly increase the probability of you becoming a data scientist.

Even a computer scientist would be happy with these odds.

What about D S availability aside, D s gives you all the right tools without forcing you to devote time and effort to acquire skills outside of your interests like software development, for example.

That also means you don't need to earn additional outside sources of credibility such as certificates for online courses.

However, a D s degree is very much tailored towards future data scientists, so its niche nous limits your possibilities of a future career change.

To summarize things we see that computer science is the current leader in the field in the demand in the job market, data science is a growing degree, but it takes time for the trend to shift.

Going by the data from the last several years, we can conclude the following.

It's very likely that if at all the pattern eventually shifts and D s becomes the best option, you'd already be working as a data scientist.

Thankfully, employers value experience and skills more than academic credentials, so it shouldn't affect your progress in the field.

So all things considered, we can confidently say that despite its small drawbacks, computer science is the better degree choice for a job as a data scientist.

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