The Emerging Field of Data Science

We sat down with UMBC alum, Donald Miner, and talked about the field of Data Science and why now is a great time to get into it.

This interview has been lightly edited for clarity and readability.

What is your role as a Data Scientist?

I oversee the business operations at Miner & Kasch. As a data scientist, I work with clients, projects, and coach data scientists. The best part of what I do is helping clients to frame their data science and machine learning problems and to figure out how they can leverage data to solve their issues.

What is most exciting about Data Science?

The most exciting thing about the data science industry is the exploration aspect of the data. There’s data everywhere and it comes from many different sources. There are so many things that live within this data. Turning that data into something that provides value is gratifying. The amazing part about data is turning it from nothing into something.  

Why is Data Science critical?

Managing data properly is really important because you want it to be easy to leverage. Organizations that manage their data well are purposeful about their data collection. When organizations are strategic about data collection, they can make better, more informed decisions based on that data down the line.

What are hiring managers seeking?

Hiring managers in data science struggle to find the right candidates because the best data scientists have skills that are hard to articulate on a resume. Meaning, many of the skills that make up a great data scientist are intangible ones, like leadership, team-focused, as well as self-driven. Hiring managers are also looking for candidates who have a strong desire to dig into data, explore the data, and be a self-starter when it comes to honing in on the value in that data.

Hiring managers search for candidates who have the correct machine learning skills and the correct data platform skills. Using the correct databases and the technologies that are relevant is challenging because they’re always changing. And so when a candidate can show they have this ability to keep up with technology’s dynamic nature, they stand out.

What does a day-in-the-life look like?

A data scientist would expect to be working, pretty obviously, with data. But what’s really important that professionals in other engineering jobs, like software engineering or traditional data analysis, don’t get to see is the human element to the data. So, a day in the life of a data scientist will include talking to people that are making business decisions and explaining what the data is revealing.

A person might spend three weeks working on a report or building a situational model and then next turn around and explain to peers collecting and analyzing the data what it all means. This is really important. There’s a strong human element to data science that is critical and incredibly enjoyable to explore. Even though the day-to-day work can feel engineering heavy, there is this whole other side to it, the human element that adds complexity and interest.

Can you talk about the human connection to data?

Data is, for the most part, created by humans, so the human element is baked into the data. Even if we’re looking at geospatial images of the world, we’re looking for things that humans have changed in the world. If we’re looking at reports, like the function and success of a telephone pole in a particular area, or more impersonal examples, like finance, security and cybersecurity data, all these elements that led to the creation of the data are human-driven.

So why is this important? Because it’s a huge translation task. You have to translate the elements into data and then translate meaningful data out of that collection of data. The best data scientists are the ones who can figure that out.

What qualities to employers seek?

The best data scientists are intuitive. They can figure out what’s going on by just looking at something very briefly, by reading between the lines, and then have the technical skills to be able to execute their vision on how to test their hypothesis.

Is an advanced credential favored?

Advanced education for data scientists is increasingly important because the field is moving so fast. There’s no way that anybody could naturally pick up all the things that need to be picked up if they’re trying to get into this career right now.

I’ve had the luck of being able to grow into this domain. As its developed, I developed at the same pace. But, for somebody just coming out of school or changing careers, it’s an impossible task to figure out on your own. Advanced education teaches you the core fundamental skills that are needed to be an effective data scientist.

How can a data scientist stand out?

Data science candidates who want to stand out should build a portfolio that shows you’re able to analyze data and draw conclusions on it. I would suggest volunteering on a project by diving into some data and building visualizations and communicating your insights on it. Then when you interview, take the project into an interview to show them your ability. This is what the hiring manager wants to see. They want to know whether you can execute on data science projects. Just having the skills is not enough. You need to have the intuition and the know-how.

Why is now is a great time to be in data science?

Now is a great time to be in data science because companies want to be doing data science. They’re looking for more data scientists because they’re basing their entire strategies around data science. It’s become the standard thing that most organizations implement. It’s early enough that you can stand out because it’s not over-saturated. There are lots of jobs available.

Data analysis is a quickly-growing field and demand for experienced data science professionals will only continue to grow. According to the Bureau of Labor Statistics, data analyst jobs are expected to increase by nearly 20% between 2014-2024 and the Baltimore-Washington metro area has seen a disproportionate rise in the number of employers seeking candidates with expertise in data science.

Are you ready to claim your future in Data Science?

Leave a comment

Your email address will not be published. Required fields are marked *