Will Data Scientists Be Replaced by AI?

byMoneerS

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Updated 2 years ago

In the past decade, the demand for data scientists has exploded, as more and more companies search to wring maximum value from their ever-growing sets of data.

But could artificial intelligence (AI) soon threaten their jobs? Recent advances in AI make this possible, with algorithms that sift through large amounts of data to find patterns and make predictions much more effectively than human beings can do.

Data scientists are well aware of the danger, but that doesn’t necessarily mean they’re doomed to be replaced by AI at some point in the future—even if that future arrives sooner than we expect.

What is a data scientist?

A data scientist is a highly skilled professional who can take large amounts of disparate data and find patterns, predict outcomes, and provide insights that help an organization make more informed decisions.

This job requires advanced analytical skills (and deep knowledge of probability and statistics), as well as programming abilities. The high-level goal of a data scientist is to use analytics to solve business problems -- whether they be predictive models, statistical analysis or machine learning algorithms.

Often, you’ll work in a team setting with other members of your organization; however, if you have a Ph.D., it's possible that you could be doing consulting on your own or as part of a small company/start-up.

What do they do ?

Well, not only does machine learning (ML) allow computers to teach themselves how to carry out specific tasks, it can also give a computer certain cognitive abilities. For example, a company called Vicarious is working on an AI system that can learn and experience like humans do.

Their end goal is for machines to feel like humans do. A research scientist at Vicarious named Dileep George says in an interview: When we see something touching, we have a physical reaction in our body. We generate emotions and feelings around it.

The role of the data scientist

The role of data scientist is a relatively new one, and many companies are currently experimenting with ways to make it their core business.

Although there’s been a lot of buzz about how artificial intelligence (AI) could possibly eliminate the need for human data scientists in future, I don’t think we’ll see it happening any time soon. It could be another 50 years before AI develops to a point where it can work on projects independently—but that doesn’t mean we should ignore AI as an option.

In fact, having humans and machines working together is more likely to produce accurate results than trying to replace one with another.

Myths and misconceptions about the role of a data scientist

1. A data scientist is a statistician: This isn’t true at all, in fact, many data scientists have no formal statistics training.

2. A data scientist is a SQL person: Nope; most data science work is done in R or Python.

3. A data scientist has to be highly technical: True but only because they want to understand what they are doing - being technically proficient gives them greater control over their experiments and results

4. A data scientist will solve your business problems: Well yes but only if you ask them to (data scientists love questions). They are problem solvers not problem finders!

The future of data science

Some claim that artificial intelligence (AI) is on a path to surpass human intelligence—that machines will eventually outsmart people at every turn.

While it’s hard to imagine that happening anytime soon, it is easy to see how automation can start replacing traditional data science tasks. The thought of losing your job to a robot might seem scary, but for most companies and industries, AI can actually help solve some of their biggest challenges and take businesses one step closer to becoming data-driven operations.

So what are some tasks that humans would typically do as part of their job that could be performed better or more efficiently with machine learning and automation? Here are just a few examples

How can you get into becoming a data scientist?

The job outlook for data scientists is pretty good. The U.S. Bureau of Labour Statistics (BLS) reports that employment of statisticians and actuaries is expected to grow 14 percent from 2016 to 2026, much faster than average for all occupations.

Employment of data analysts is projected to grow 31 percent over that period, much faster than average. This career requires a bachelor's degree in mathematics, statistics or a related field such as computer science or information systems along with relevant work experience, typically one to three years, which could include internships while in school

It’s not about being replaced, it’s about how you work with AI tools

People who use AI tools to augment their abilities, rather than replace them, will likely see an increase in productivity. But working with your tools shouldn’t be optional — it should be a necessity.

After all, AI is nothing without us. As humans become more comfortable with working alongside machines, we have an opportunity to spend our time focusing on higher-value activities that computers aren’t able to do for us just yet.

We can leverage AI as a tool and spend our time using our human skills instead of trying to imitate what computers can do.

Is there anything better than a human brain?

If you were to ask many people whether they thought data scientists would be replaced by artificial intelligence (AI), most of them would probably say no.

However, there’s a lot of evidence that suggests otherwise. At first glance, it might seem difficult to imagine how a computer could come up with insights as good as those a human can, especially when you consider how complex it is for us to arrive at conclusions and analyse large amounts of data.

But while we humans may be fantastic at thinking outside-the-box and uncovering hidden patterns in large datasets, we’re also terrible at arriving at concrete answers. What good are hypotheses if you can’t test them without access to thousands or millions of records?

Only time will tell

The truth is that even today, we don’t have enough data to know whether or not deep learning techniques will be able to replicate human decision-making processes. We simply can’t run large-scale experiments on them because they work so well and are improving so quickly.

But while no one knows how much they’ll be able to do in ten years, it seems reasonable to assume that data scientists—who leverage large amounts of human capital as part of their process—may soon become a relic of another era.

Only time will tell if people can beat computers at their own game—but either way, there will always be a place for human analysts when it comes to making sense of all that data.