Are Data Science Careers On the Rise in 2020?

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Data science careers were undeniably among the top choices for young professionals in the last few years.

According to Glassdoor, 2016 was the first year in which “data scientist” was the highest paying 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 are data science careers worth pursuing in 2020? And if they are – why and for how long?

In this article, we’ll explore the answers to these questions. We’ll take a look at the current status quo of the demand for data science talent; we’ll explore how 3 leading companies utilize data science to advance their business. Finally, we’ll see if the supply of data science professionals has already caught up with the demand and how the current trend will shape the future of data science as a career.

Let’s dive in!

Are Data Science Careers on the Rise in 2020?

The answer to the first question is simple. Yes, data science careers are without a doubt still a synonym for success.

Here are the reasons why:

Data science, like any other business-related phenomenon, 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 or a web-designer, or anything, really, that would allow them to be in the computer science industry. Salaries were terrific and being there was considered an exceptional opportunity. As time passed by, the supply of CS guys and girls started to catch up with the demand and salaries plateaued. However, 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 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. So, it’s quite understandable why 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, in the following paragraph we’ll explore this relationship further, and how it applies to data science in particular.

Where Does the Demand for Data Science 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 brilliant 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.

google-data-science-career

Google is the embodiment of data science. Everything they do is data-driven. From their search engine – google.com, through 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 famous Forbes ranking.

Since Google is just one shining example, it’s only right to also mention Amazon and Facebook.

Let’s continue with Amazon.

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. Тhat way, when you actually purchase it, it is delivered the same day. Happy customers are loyal customers and Amazon knows that.

What about Facebook?

facebook

Well, to begin with, it is very important to note that Facebook is not just Facebook, but rather Facebook, Messenger, WhatsApp, and Instagram… for now.

And Facebook is generating ad revenue like crazy, as it has all that personal data for all its users.

Most of us interact with all their platforms all the time. That 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, and 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. So, consequently, they can target you, and all their users, in extraordinary ways, securing unprecedented marketing success for various companies. It’s not a stretch to imagine why companies just love using Facebook as an advertising medium. And that leads to Facebook generating even more data about people while, at the same time, getting paid for it!

But not only huge companies have a data science division.

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

What’s the Supply of Data Science Professionals?

As we already mentioned, the supply is not as flourishing.

Data science emerged thanks to technological change. In fact, it was impossible for it to exist 20 years ago because of slow computers, 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. 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. Nevertheless, it seems there are still not enough people exploiting the opportunities in the data science industry.

In Conclusion

Keeping in mind that the demand will continue to grow, we can expect outcomes similar to the computer science field – demand will continue to outgrow the supply for a very long time, maintaining data science careers as some of the most lucrative choices.

All things considered, data science is going stronger, both from a company’s perspective and from the perspective of a job candidate. So, this really is the best time to break into the field!

Ready to take the next step towards a career in data science?

Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses; build up step-by-step experience with SQL, Python, R, and Tableau; and upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. If you still aren’t sure you want to turn your interest in data science into a solid career, we also offer a free preview version of the Data Science Program. You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to see if the program is right for you.

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