In this video we will explore if “Data Science really is a rising career”, and if it is

– why and for how long. The answer to the first question is simple:

yes, data science is without a doubt a rising career.

According to Glassdoor, 2016 was the first year in which “data scientist” was the

‘Best job’ on the market. And after that? Well, it was in the lead in 2017, 2018, and

2019 as well! With a mean base salary of more than $100,000, being a data scientist seems

like the dream job of this century.

But why is that? Of course, like any other business-related

phenomenon, it follows the basic laws of economics – supply and demand. The demand for data

science professionals is very high, while the supply is too low.

Think about computer science years ago. The internet was becoming a “thing” and people

were making serious cash off it. Everybody wanted to become a programmer, a web-designer

or anything, really, that would allow them to be in the computer science industry. Salaries

were terrific and it was exceptional to be there. As time passed by, the salaries plateaued

as the supply of CS guys and girls started to catch up with the demand. That said, the

industry is still above average in terms of pay.

The same thing is happening to the data science industry right now. Demand is really high,

while supply is still low. And, as stated in an extensive joint research performed by

IBM, Burning Glass Technologies, and Business-Higher Education Forum, this tendency will continue

to be strong for the years to come. This, by itself, determines that salaries

will be outstanding. Consequently, people are very much willing to get into data science.

Of course, this supply-and-demand discussion is not all that informative without the proper

context. So, let’s explore this relationship further, and how it applies to data science

in particular. First, where does the demand come from?

That’s fairly straight-forward. Data-driven decision-making is increasing in popularity.

While in previous years, analysts would use software like Excel to analyze data, and only

academics would turn to SPSS, and Stata for their statistical needs, now ‘the times

they are a-changin’, and almost anyone can have access to and use of a data-crunching

tool.

In fact, advancements in technology have brought about things like:

 Cloud-based data services for your digital marketing efforts such as Google Analytics;

 Complicated ERPs that breakdown information and create visualizations; examples here are

SAP and Microsoft Dynamics used heavily by business analysts, HR, supply chain management,

and so on;  Tableau and Microsoft Power BI for your

business intelligence needs; with these tools, analysts can visualize the data in unprecedented

ways and uncover unexpected insights;  And, of course, there are also outstanding

improvements in programming languages like R and Python, which let you perform very complicated

analyses with just a few lines of code. So, you have all these tools that are not

that hard to use. You can afford to employ some people to take advantage of them, and

you know that this will quadruple your business. Would you get a data science team? Absolutely.

So, what are some examples of “data science fueled” enterprises in the real world?

Well… Google for instance. Google is the embodiment of data science.

Everything they do is data driven. From their search engine – google.com, through their

video streaming service, a.k.a. YouTube, to maximization of ad revenue with Google Ads,

and so on. Even their HR team is using the scientific method to evaluate strategies that

make the employees feel better at work, so they can be more productive. Not surprisingly,

Google has been rated number 1 employer for 3 years in a row, according to the renowned

Forbes ranking.

When talking about Google, it’s only right to also mention Amazon and Facebook. Let’s

start with Amazon. I believe you are well-acquainted with how

Amazon works. You go to Amazon.com for some item; you usually buy it and then... you somehow

end up buying tons of other stuff you didn’t even know you needed. Actually, each product

recommendation that you get comes from Amazon’s sophisticated data science algorithms. In

fact, Amazon has implemented an algo that can predict with great certainty if you are

going to buy a certain product. If the probability is high enough, they may move the item to

the storage unit closest to you. This way, when you actually purchase the product, it

is delivered the same day. Happy customers are loyal customers and Amazon knows that.

What about Facebook? Well, to begin with, it is very important

to note that Facebook is not just Facebook, but a bunch of websites and apps, most notably

Facebook, Messenger, WhatsApp, and Instagram… for now.

And Facebook is generating ad revenue like crazy, since it has all that intimate data

for all its users. Most of us interact with all their platforms all the time, which means

that Facebook knows if we prefer cat videos or dog videos; by extension, they now know

if we are cat people or dog people. They know what sports we are into, what food we prefer.

These facts may sound trivial, but if you interact with certain clothing brands, for

example, Facebook will also know your preferred price range, or in other words – the amount

of money that you are willing to spend online. This way, they can target, you, and all their

users, in extraordinary ways, securing unprecedented marketing success. It’s not a stretch to

imagine why companies just love to use Facebook as an advertising medium. And once they do,

do you know what that means? Facebook generates even more data about people and they even

get paid for it!

That being said, not only huge companies have data science departments. Small businesses,

blogs, local businesses… all use Google Analytics for their needs and make huge gains

off it. This is also a part of data science. You don’t need to do machine learning to

monetize on data science. I understand that some of you may not be convinced

just yet. However, if your competitors are relying on data-driven decision-making and

you aren’t, they will surpass you and steal your market share. Therefore, you must either

adapt and employ data science tools and techniques, or you will simply be forced out of business.

That’s the reality of the demand for data science.

This brings us to the supply of data science professionals. As we already mentioned, the

supply is not as flourishing. Data science emerged thanks to technological

change. It was impossible for it to exist 20 years ago because of slow internet connection,

low computational power, and primitive programming languages.

However, when data science did come about, traditional education was simply not ready

to meet this need. Data science is still a relatively new field and there are still very,

very few programs that educate the aspiring data scientists. In fact, research suggests

that the people that get into the field, usually transition from some other field and gain

the necessary skills mainly through self-preparation. That includes books, research papers, and

online courses. You can find a link to that study in the description. But if you’re

not into reading over the findings just now, the summary we can offer is that overall,

it seems there are still not enough people exploiting the opportunities in the data science

industry. Now that’s the 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. 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.

Going back to Economics 101, if you have a low supply of labor, the salaries will maintain

high.

And, keeping in mind that the demand will continue to grow, we can expect that the result

would be something like the computer science field – demand will continue to outgrow

the supply for a very long time, maintaining the data scientist as one of the most lucrative

career choices.

And, yes. Data science is on the rise, both from a company’s perspective and from the

perspective of a job candidate. So, this really IS the best time to break into data science!

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data science studies!