Is Data Science Really a Rising Career in 2020 ($100,000+ Salary)


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! If you liked this video, don’t forget to
give it a like, or a share! And if career insight about data science is
what you’d like to learn more about, please subscribe to our channel.
Thanks for watching and good luck with your data science studies!

35 Comments

  1. I'll start my Bachelor in DS from March& can't wait to dive in*-* allready finished a 'Bootcamp' course on Udemy& studying Python everyday

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    #data #science #career #salary #datascience #datasciencecareer

  3. Awesome video, I am planning to buy your udemy course on Datascience, is this course enough to get an entry level job in the field. Thanks Jamuna

  4. I'm a mechanical engineering graduate but I'm in dilemma wheather I should switch my career into data science or not.

  5. I love you guys. Subscribing to 365datascience track was the best thing that happened to me in 2019 and gave me so much mental strength when I lost my previous job! It was my first contact with data science and now I can dream of a truly ambitious future for myself in the field of Analytics ❤️

  6. Gonna start the 365 data science program. I studied Neuroscience at the University of Michigan intended to do pre med but hated chemistry, working with a professor on a new idea called Neuroepistemology connecting his newest research to a reformulation of philosophy much like Neuroeconomics did with economics but I think of it more as a hobby because spending my life torturing rats for science isn’t my aspiration and working on abstract models are fun but won’t get me paid haha unless I become a big deal. I’m a big fan of math and was always in advanced courses, but I get very apathetic taking it for a grade. I’ve had stints with R, SPSS, Matlab, and Python but only surface level for classes. I’m actually licensed to do architectural plan review and as a building inspector. My real passion is physics but that bus is clearly gone. I’m all over the place, but I think this is a good starting point if I want to switch over to Data Science at some point in the future. The architectural plan review path is my safe back up because it’s basically a virtually zero supply industry, the only thing is the demand isn’t huge. But in comparison to supply it may as well be infinite. Thanks ! *Udemy and Coursera I’ve known about since high school but I think the freedom to choose from many classes made it hard to focus on one. In this program I’ll know that there’s many classes but they all have one goal. I’ll still probably start a few Coursera classes like the Python Data Structures 5 part course.

  7. I have not completed my bachelors degree in social sciences but i really want to 'hack my career' to become a data scientist because i love mathematics. This video is so encouraging. Just started coding in PYTHON though.

  8. Can someone help me here…. Can someone suggest me Universities in US, Canada or Australia from where can i pursue my master's in data science.

  9. Well, Data Science is just a term. You will actually work in sub-areas: Business Inteligence, Data Analysis, Data Engineering (which has Big Data inside), Machine Learning…
    the truth is that millions are studying those fields, but more than 90% of those will fail cause they have no programming skills for stuff closer to Data Engineering and people have no math skills for the Data Analysis jobs.
    The companies won't even check your application if you do not have a computer science degree or a statics degree. I am sorry to tell you the truth.
    You can learn the basics of Python and R, but you'll need to work also with other programming languages and frameworks and other teams, so don't think that buying Moocs will save you.
    So, get a degree in Computer Sciences and perhaps in Math or Statitiscs (for the Data Analysis and BI parts).
    I am sorry to tell that, but companies will only listen whatever you've got to say if you have a good degree. They won't let someone who took online courses to manage their data.

  10. Do firms prefer computer science degree over information system degree? I think information system is more relevant to data science

  11. As an established data scientist, I see a huge influx of applicants for data science positions. However, very few of these aspiring data scientists have a sufficient portfolio to warrant consideration for the roles they are applying for. I highly recommend that you focus on projects and building out your github if you want to get one of these jobs. I have a few videos about data science projects on my channel for those who are interested in learning more!

  12. I did enroll for 365 Data Science boot camp thru Udemy with killer price. With that being said, it is only useful for guys with other main specialties with instant needs for data manipulation and analysis. I am good with SQL, VBA and Power BI plus a CPA and FRM certificates. Now need more practice on real projects to test various statistical models. In short, it ain’t that easy to be a true data scientist

  13. Can someone with "Senior Test Engineer" designation eligible for data science interview… assuming he or she did a PG course and posses skills. Will the previous designation matters in resume filtering. Your thoughts please

  14. The video makes it sound like there is a shortage of supply. Without real experience, it is very hard to do anything on my own. With enough training and experience, one can reach the level you call as one of the "supplies". But that's based on its high standard. In that case, there is a shortage of everything not just data scientists.

  15. The demand for EXPERIENCED Data Scientists is high. The supply of entry level DS, especially in big US cities is way higher than the demand, resulting in a big bottleneck. That's why it's important to have one hell of a Resume & Portfolio as a Junior to distinguish yourself from the others. This video makes it sound like you're gonna get a thousand offers instantly anywhere and I thought I'd correct that. Videos like these never talk about the delta between entry level and senior level…

  16. The OGs of data science are researchers and statisticians. Having a statistics or applied statistics background as an undergrad, then go for a master's again in an applied field will get you further than a pure computer scientist or engineer. From my experience and what I've seen computer scientists and engineers just throw machine learning at all problems without carefully considering what the data is really about. Statisticians and researchers carefully look at data.

  17. Yes supply is high, but it’s a bs wack/lame nerd job man. You don’t want to do it.

    I deal with large data currently

  18. There's a difference between encouraging people to get into a field that has the potential to give them financial stability and…literally standing in a pile of money, throwing coins in the air like Scrooge McDuck. I hope the graphics team had fun? But that's not an expectation i have as a data science student.

  19. It seems like a great career. I’m a financial analyst right now but I already know SQL and Power BI. I’d like to learn Python and R so I can move to a role where I can combine my existing finance knowledge with data science

  20. Can you become a Data Scientist with a Bachelor in Legal Studies if you complete a Data Scientist course like this one?

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