Hi everyone! Welcome to another 365 Data Science
special!
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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Thanks for watching and good luck with your
data science studies!
