Will There Be a Shortage of Data Science Jobs in the Next 5 Years?

By Pranjal Saxena, Data Scientist, Top Writer in Artificial Intelligence

Photo by Andrea Piacquadio from Pexels

 

I have been in the data science field for the last half-decade when python programming came into the trend. Back then, in 2016, neural networks and deep learning were just some buzzy words. At that time, there was a hype about Google self-driving cars and reinforcement learning. But, most of the data science enthusiasts were not even aware of the working of neural networks.

Today in 2021, most companies are adopting a data science strategy to make more revenue by automating different scenarios and replacing dozens of IT people with a single data scientist who can automate the task of those IT people using various automating tools like BluePrism, UI Path, Python and machine learning algorithms.

That’s why most of us are working hard to learn python, machine learning, analytics, deep learning. Why? Because there is an excellent value for the data scientist in the industries. And, also people are getting a good hike in their job data in the data science field.

But, do you know in today’s time, these “automation tasks are being automated using another automation strategy?” The whole data science pipeline is being automated using a single tool.

In 2019, data scientists used to spent days in data gathering, data cleaning, feature selection, but now we have many tools in the market that can do these tasks in a few minutes.

On the other hand, we were trying different machine learning libraries like logistic regression, random forest, boosting machines, naive Bayes, and other data science libraries to a better model.

But, today, we have tools like H2O, PyCaret, and many other cloud providers who can do the same model selection on the same data using the combination of other 30–50 machine learning libraries to give you the best machine learning algorithms for your data with least error.

Things are now getting change at a fast pace. And, we are anyhow losing our value because everyone will trust the tool that tries more than twenty machine learning algorithms to come up with better accuracy than us who tries only a couple of machine learning libraries to come up with less accuracy.

 

The tough reality part

 
Until now, we have discussed how some automation tools are doing well in the machine learning area. And these tools are doing well than us because where we are using limited machine learning algorithm knowledge. In contrast, these tools are using the combination of libraries to get more efficient results by automating the complete EDA process providing the best possible results in less time.

But, what about the deep learning area where we have less command than the machine learning area and having limited processing power. There also we have a good amount of tools in the market. These tools invest a good amount of money in having the best processors.

Deep learning is all about more data, processing power, and a complex neural network that needs more processing power to provide more accurate results.

When we talk about deep learning, that is famous for handling unstructured data. And, 95% time, we work with images and test data here. Object detection, image segmentation, building chatbots, sentiment analysis, document similarity are the famous use cases.

But, working on these use cases required knowledge of different deep learning algorithms like convolution neural network, recurrent neural network, U-Net, hourglass, YOLO, and many more models that need a good amount of processing power to process more data for better accuracy.

The catch here is that when in today’s time in 2021, companies are investing a good amount of money in automating these complete pipeline workflows. And, we are busy understanding basic machine learning and deep learning model irrespective of the fact that we can’t afford high-end machines without any investors.

Each company is aware of this fact, so after five years, when these cloud-enabled data science tools will become more efficient and will be able to provide better accuracy in much less amount time, then why companies will invest in hiring us and not buying the subscription of those tools?

 

The Ray of Hope

 
When all these things are going to automate, you might be thinking about the future of data science enthusiasts. Will, there be a shortage of jobs or will there be fewer hirings?

Well, things become easier when we think differently. It is true that companies will keep focusing on the automated workflow of machine learning. But, remember, no company wants to depend on another company for their work.

Each company aims to build their product so that instead of depending on others, they can build their automated system and then sell them in the market to earn more revenue. So, yes, there will be a need for data scientists who can help industries build automation systems that can automate the task of machine learning and deep learning.

At last, we can say that the role of data scientists will be to automate the pipeline with optimized results. So, in the end, we will be automating the pipeline of machine learning workflow and let the automation decide the best features in the data and derive the best possible result using the best-curated algorithm.

 

Final Thoughts

 
We have seen how there will be a lack of data science jobs in the next five years because companies will be adopting the automated pipelines of data science. But, there will also be high demands for data scientists who can automate data science pipelines.

As per my thought to automate those pipelines, we first need to understand machine learning algorithms to build a better automated system, which will eventually lead to more jobs.

Well, What are your thoughts? I would love to hear yours. I hope you liked the article. Stay connected for more related articles. I publish articles on real-time data science scenarios and their use cases.

Thanks for the reading!

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Original. Reposted with permission.

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