What is Data Science?

Data science is a term you’ve probably heard at some point in your life, especially if you’re in tech. But what is it really? Is it about creating dashboards? Is it about making machine learning models?

Well, data scientists definitely know how to do both of these things, but that’s not the right way to define data science. You might also be wondering what’s the difference between data science, data analytics, and data engineering.

All of these questions will be answered in this article, so let’s get started.

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Way more than just dashboards…

To define data science, I'll quote Joma Tech from his YouTube video, which I highly recommend watching: What REALLY is Data Science? Told by a Data Scientist, where he says:

“Data science is not about making complicated models. It’s not about making awesome visualizations. It’s not about writing code. Data science is about using data to create as much impact as possible for your company.”

I stumbled upon this video when I was first discovering the world of data science, and I believe it is the best I’ve ever seen at explaining this. In fact, the Internet is full of misconceptions about this definition, and limiting data science to the tools used in it nowadays ignores the fact that it has been around for hundreds, or even thousands of years; it just didn’t have this name yet.

So, if data science isn’t about the tools you use, what is it about then?

Data science is the process of analyzing data and taking valuable insights from it. It’s giving meaning to data, and creating a plan of action that is as impactful as possible based on that.

How it all started

Humans have been using data science for a long time, collecting, manipulating, and analyzing data to make better decisions. But it was only in the 20th century that people came up with this term to describe a couple of different things.

Some wanted to use it as an alternative name for computer science; others wanted statistics to be renamed data science; and others wanted it to be a brand new concept.

What happened next is that data science actually became a new concept. With the advent of Web 3.0, an enormous amount of data started being collected by companies in such a way that classic data manipulation methods wouldn’t do it anymore. That’s when the term big data started being used, and big data needed to be handled and analyzed in new ways.

As a result, several techniques were developed to handle all of this data, and people started talking about data science as we know it today.

What’s the difference?

The question lingers… what’s the difference between data science, data analytics, and data engineering?

We could say that data analytics and data engineering are subfields of data science. Here are the overall concepts:

• Data Analytics: It is related to the exploration, analysis, and interpretation of data to gain insights and support decision-making.

• Data Engineering: It involves creating infrastructure for storing, extracting, cleaning, manipulating, and managing data.

Conclusion

Even though data science is something people only started talking about in recent years, its core fundamentals have always been applied. Analytical thinking, strategy, and creativity all characterize the art of data science, and humans have been using them to solve problems long before machine learning, Power BI, and Tableau were a thing.

And did you notice? From thousands of years ago until today, those are still super relevant skills. So data science is a lot more about knowing how to use them than anything else.

So, let me know: what do you think are the main skills that a data scientist should have?