Rajendra

I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing ...

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I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing

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Why Data Science Is Such A Hot Career Right Now

By Rajendra |Email | Oct 26, 2017 | 10029 Views

According to Glassdoor, in 2016 data science was the highest paid field to get into.

Of course, this follows the basic laws of economics - supply and demand. The demand for data science 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 a lot of money on it. Everybody wanted to become a programmer, a web-designer or anything, just to be in the CS industry. Salaries were super high and it was exceptional to be there. As time passed by, the salaries got lower 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 and supply is really low, so the salaries are still very high and people are very much willing to get into data science.

Let's explore the supply and demand for data science for a bit:

Demand:

Data driven decision making is increasing in popularity. While in the past years, analysts would use software like Excel to analyze data, while only academics would turn to SPSS, Stata, etc., now things are changing.

Technology is advanced enough to have tools like:

  1. Google Analytics for your marketing department.
  2. Complicated ERPs that breakdown information and create visualizations. Examples are SAP and Microsoft Dynamics for your business analysts, HR, supply chain management, etc.
  3. Tableau, Sisense, Microsoft Power BI for your business intelligence department (which is a sub-field of data science), where analysts can visualize the data in unprecedented ways and uncover unexpected insights.
  4. Outstanding improvements in programming languages such as R and Python, which let you perform very complicated analyses with a couple of lines of code.
  5. So, you have all these tools that are not too 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? Of course.

What are some examples of data science?

Google. They are the definition of data science. Everything they do is data driven from their search engine (google.com), through their YouTube efforts, maximization of ad revenue, etc. 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. Google is not the best place to work just by chance.

Amazon. Each product recommendation that you get comes from Amazon's sophisticated data science algorithms. Actually, Amazon has implemented an algorithm that can predict with some certainty if you are going to buy a certain product. If the probability is high enough, they move it to the storage unit closest to you so when you actually purchase it, it could be delivered the same day.

Facebook. Facebook is generating ad revenue like crazy since it has all that personal data for all its users. Since you interact with the platform, they know if you prefer cat videos or dog videos, so they know if you are a cat person or a dog person. They know what sports you are into, what food you prefer, the amount of money that you are willing to spend online. In this way, they can target their users in extraordinary ways, thus companies just love to use it as a medium.

That being said, not only huge companies have a data science division. Small businesses, blogs, local businesses,etc. use Google analytics for their needs and have seen huge gains from it. This is also a part of data science. You don't need to be doing machine learning to monetize on data science.

Now, 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.

Supply:

Data science was driven by technology change, thus it was impossible to exist 20 years ago (slow computers, low computational power, primitive programming languages, etc.)

However, when it came about, traditional education was not ready, so there are still very, very few programs that educate aspiring data scientists. I would say that the people that get into the topic, usually transition from some other field and gain the necessary skills mainly through self-preparation. That includes books, research papers, and online courses. That said, there are still not enough people exploiting the opportunities in this industry.

Having a low supply of labor, salaries will remain high. Thus, this is a good field to get into.

Conclusion:

Keeping in mind that the demand will continue to grow, I expect that the result would be something like the CS field - demand will grow faster than the supply for a long time.

So, yes, data science is on the rise, both from a company's perspective and from an employee's perspective. This makes data science a great field to get into at the moment.

Source: Forbes