Data Science Isn’t Dying — It’s Evolving: How AI Is Reshaping the Role

Source: Created with MistralAI

When AI tools like ChatGPT, Claude, or Deepseek become more popular, you might wonder why systems still need us humans to fulfill the tasks that AI can easily do for lower costs and faster, without resting. This might repeatedly increase the fear that the age of data science is dead. But is it?

After reading public interviews with NVIDIA’s CEO and Mark Zuckerberg, I thought the same thing. What did they say? In February 2024, NVIDIA’s CEO said that coding is dead, and Mark Zuckerberg said that by mid-2025, AI agents would have capabilities similar to mid-level software engineers.

What does this mean? Does that mean data science is dead? No, not at all. It has been evolving, and it is big. We can say that we are on the edge of the most significant evolution that the tech industry has ever faced. To keep up as data scientists, we must evolve with it, and in this article, we will discover how.

The Evolution of Data Science How has Data Science changed?

Engineers would know that there is a thermodynamic law called entropy. Based on this theory, the universe has no stillness. Similarly, there has never been stillness in data science, too, how? Let’s see.

Ten years ago, there were few data visualization tools. Data scientists often relied on more basic tools like Python, R, and Excel to create visuals. However, with new tools like Power BI or Tableau, data visualization, a part of data science, changed. Those who equipped themselves with this knowledge became data professionals.

Similarly, machine learning, a famous discipline in data science, is no longer just about building an algorithm — AI can do it in seconds. Let’s prove this by sending this prompt back to ChatGPT.

Build 5 different machine learning algorithms on the Titanic dataset to predict the survival rate and compare the results in a bar graph.

Here is the output.

You can see the results in seconds. Let’s go further and create a portfolio project from this. Sounds interesting, right? Here is the prompt that I developed for this:

Create a portfolio project that includes
Data Exploration
Data Cleaning
Data Manipulation
Data Visualization
Machine Learning
Add relevant explanations for each section along with codes. Send the output in Canvas.

Let’s see the output.

As you can see, it generates a small project, including codes. Interesting right?

Why Data Science Is Not Dying: How AI Can Contribute but Not Conquer

As you can see, AI can handle a data project like this. It is a cool way to automate tasks. However, it can not think critically, decide independently, and be creative.

For instance, when I tried to run this data project, it tried to load the Titanic dataset from GitHub, but it failed.

Next, I should lead it to find this dataset from the web here.

It could not load the dataset from Seaborn or OpenML. Why? There could be many reasons, but humans are needed to find them and avoid future errors.

AI Still Needs Humans

AI has been trained based on historical and biased data. But let’s say we are going to build a predictive model. AI will select the data it uses, but it is biased in the first place, so how would it choose?

There should be a human who is directing the wheel, who is aware of data science, machine learning, or whatever the topic is, and who should direct the AI towards the goal.

The Human-AI Partnership: Working Smarter, Not Replacing Jobs

I remember AI news that says AI can predict kidney failure 6 times faster than normal humans. Here is the article.

In this article, researchers from the University of Sheffield developed an AI tool to predict kidney failure in ADPKD patients. The AI automates MRI scan analysis, measuring kidney volume in under a minute — six times faster than human experts with the same accuracy.

Human and artificial intelligence should cooperate deeply. Artificial intelligence depends on human knowledge for training, validation, and decision-making, even while it accelerates everything and raises efficiency. In this case, human analysts first tracked kidney scans to teach artificial intelligence so it would learn to do segmentation precisely.

• AI can accelerate model training, but human decisions still guide the process.
• AI can generate insights, but humans must place them in the right business context.
• AI can produce reports, but humans are still needed to craft the narrative

The consensus? AI is a tool, not a replacement. But what are the new skills that data scientists need?

New Skills Data Scientists Need

If you check the roles, the name of the Data Science might not be as much as it was a few years ago, but you can see names that have transformed from the Data Science because the industry need has not changed, but the tools have.

That’s why Data Scientists must now position themselves as professionals who can use AI to enhance their tasks as we did in Machine learning. The task might be different, but you get the idea. You should be in a position where you can use AI as a tool that accelerates your task.

And I assure you that your portfolio will need one or more AI projects that show you now how to use AI.

How to Stay Relevant in Data Science

The skills that landed a job five years ago won’t be enough tomorrow. With AI automating various technical tasks, the real question is: how can you stand out in an evolving industry? The key to success is constant learning, adaptation, and positioning yourself as someone who doesn’t just build models but makes a real impact.

But let’s see how you can do this.

Always Be Learning

You will fall behind if you do not learn. This is true not only for the future but also for today: keeping up with new tools, algorithms, and trends is vital.

How to do it?

• Follow professionals on LinkedIn, Medium, and research papers to stay informed.
• Take courses on AI, MLOps, and cloud computing to enhance your skill set.
• Experiment with new tools like AutoML, LangChain, and vector databases to stay ahead.

Acquire Business Sense

Data science is about using data as a strategy, not only about analytics. Businesses need experts able to convert raw data into sensible commercial decisions.

How to improve?

• Identify key performance indicators in your industry.
• Get to know the business’s problems by working closely with its marketing teams.
• Focus on delivering value, not just building technical solutions.

Build a Personal Brand in the Data Science Community

Networking is about visibility, learning from others, and generating possibilities — not only for job seekers. Participating in the data science community helps one to discover some of the greatest professional breakthroughs.

Ways to stand out:

• Write blog posts about your experiences (training tips, interviews, or lessons learned).
• Join open-source GitHub communities for collaboration and exposure.
• Speak at meetups, webinars, or data science conferences to establish authority.

Work With AI, Not Against It

AI is a tool for enhancement; it is not the enemy. Find ways to include artificial intelligence into your process for more output rather than concentrating only on creating new models from scratch.

How to do it?

• Use ChatGPT or LLM tools to speed up coding and debugging.
• Explore AutoML and AI-powered analytics to enhance your efficiency.
• Focus on skills AI can’t replace, like critical thinking and decision-making.

Keep Hands-on and Keep Building

Practical knowledge is more important than theoretical knowledge. Businesses want your ability not only of knowledge but also of creativity.

What to focus on:

• Build real-world projects that solve actual problems.
• Work on end-to-end solutions, not just model-building.
• Show your work in a portfolio to stand out in job applications.

Conclusion

When new technologies come along, many people get scared. We have lived in this way since computers and the Internet came. Will it take up my job? Will my job no longer exist? As long as the industry has this need, it will need data science a lot in the future because the amount of data that has been collected is increasing. They will need data science, but due to the technology, it should evolve with it.

You must adapt to these changes, learn, and grow to keep up!