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Do Not Let the High Number of Data Scientists Make You Feel Late for the Party

There is and will be enough room.

Photo by Brooke Lark on Unsplash
Photo by Brooke Lark on Unsplash

Data Science is one of those things that experienced a tremendous popularity increase in a short time. This is an identifying characteristic of hype. Some might even think that data science is a hype that awaits its dreadful end.

I want to state my opinion first so that you know what to expect in the rest of the article. Data science is absolutely not hype. It has been around for a long time but in a different costume.

People, or at least scientists, always knew the importance of data. One of the brilliant examples is the diagram of Florence Nightingale, who is described as a pioneer in the graphical representations of statistics. She created a data visualization that demonstrates the death causes of the British Army from 1854 to 1856.

We hear about data science, or data in a broader sense, much more than we did 10 years ago. The main reason is that we are capable of managing data more than ever. To be more precise, it has become extremely easy to collect, process, transfer, and analyze large amounts of data.

My data science journey started in the beginning of 2019. As I got more involved in the data science community, I felt like I was late to start learning data science. At first, I thought I did not have enough time to close the gap with my fellow data scientists and get a job.

There was a high number of data scientists, analysts, and Machine Learning engineers all around. I was also actively following the open positions and there was a high demand for these professions.

If you took an introductory economy class in college, you would know about supply-demand curve. If not, you would still make sense of it because it is one of the most logical and intuitive concepts in science.

For goods and services, if supply becomes more than the demand, the price tends to decrease. In our case, supply is the number of data scientists, and the price is the value of a data scientist. Of course, the demand is generated by businesses that need them.

I was hearing rumors about how the demand for data scientists was getting closer to a saturation point. In "some" amount years, we would not need as many data scientists.

Despite such concerns, I kept on going. My motivation and enthusiasm to become a data scientist helped me overcome my concerns. Besides, as I learned more, my journey turned into a more exciting path.

I did not let the high number of data scientists make me feel late for the party.

And I’m glad I did not let that happen. It’s been almost two and half years since I took my first step into the field. The demand for professions in the data science ecosystem has been increasing constantly.

If you are new in the field or plan to start learning about data science, you might feel the same concern. You are likely to see lots of "data people", both experienced and newcomer. The rumors about less demand for data scientists in the future will probably make you feel even more concerned.

In my opinion, the demand for data scientists will continue to increase, at least in the foreseeable future.

Data science does not focus on a particular field. In fact, it can be applied to any business in any industry where we can collect data. Since the ability to collect and process data has become extremely easy, the scope of data science is likely to cover a broader range of applications.

The number of software tools and packages used in the data science ecosystem have been increasing as well. Such tools expedite and ease the operational work. For instance, you can apply 10 different algorithms with various different settings in a few lines of code.

I think these tools have the potential to cause a misconception. Yes, they make the operational work easier. As a result, some think that we won’t need as many data scientists as we do now. However, this is definitely not the case.

First of all, the role of a data scientist is not limited to applying machine learning algorithms. If we think of a data science product as a pie, machine learning part is just a slice. Besides, some of the problems solved by data science do not require a machine learning algorithm.

The remaining slices of the pie are just as important as the machine learning slice. For instance, a data scientist needs to frame the problem first. Then, the data requirements are defined. We cannot just use any data to solve a problem. It is the job of a data scientist to decide what kind of data or features will provide more informative power or be valuable.

The raw data is not always in the most appropriate format. Data scientists process the raw data to make it more useful. They derive new features that potentially provide more insight.

The focus of this article is not what data scientists do. I just wanted to emphasize that the software tools and packages cannot replace data scientists. They are created to support "data people" by making the operational work easier.


Final thoughts

If you plan to become a data scientists, you should be prepared to study hard. It is not easy to land your first job. It takes dedication, time, and effort.

What makes it hard to find your first job is not the high number of data scientists around you. Think of them as a sign of the popularity of data science. Do not let them discourage you.

What I think makes it difficult is the characteristics of the field. Data science is an interdisciplinary field and covers a set of skills. However, with enough effort and motivation, it is an achievable goal to become a data scientist.

Thank you for reading. Please let me know if you have any feedback.


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