As director of Notre Dame’s online master’s in Data Science program, I’m frequently asked, “What’s the difference between a data scientist and a data analyst?”

A quick scan of job postings or lists of the hottest data jobs reveals that there’s no one set definition of “data scientist” and “data analyst.” Businesses and organizations tend to define these roles in a variety of ways. As such, the exact difference is currently a gray area.

With no set definitions, what’s the best way to answer the question of data scientist versus data analyst?

Understand What Companies and Organizations are Looking For

While the definitions of these two data jobs are not set in stone and some of the day-to-day activities between data analysts and data scientists are the same, the leadership competencies required to be a data scientist are different.

The first thing to consider is what companies and organizations are looking for when it comes to managing Big Data. They are seeking to derive value from that data. In order to extract value from data, the skills a data scientist brings to the table are critical.

Despite what you may have heard, data scientists are not statisticians who live in San Francisco. Data scientists typically possess sophisticated data visualization skills and business acumen. The technical and machine learning skills data analysts and data scientists possess are not truly meaningful if teams cannot derive value from these data sets, and that’s where a data scientist comes in.

According to an article from Robert Half, data science job descriptions reveal that companies and organizations are looking for data scientists to “translate big data’s story.” Typically, a data analyst is more focused on the known, i.e., analyzing historical data from a single source, such as business analytics software, and less focused on communicating with data to internal stakeholders.

In order to extract value from data, data scientists are required to communicate in a way that makes sense to non-technical audiences across business functions (from sales to product to customer service teams). Companies and organizations also rely on data scientists to consider how they can execute business strategy in an ethical manner that takes into consideration the human impact of data usage.

The Skills Needed to Become a Data Scientist

Therefore, when it comes to evaluating data scientist versus data analyst roles, I encourage data scientists “in the making” to think about the skills they need in order to manage data and communicate with data in ways that drive crucial business strategies.

Data scientists need the necessary technical skills to apply sophisticated computational techniques in the analysis of large amounts of data with the intention of solving a business challenge. But they also have to communicate what those challenges are, how their data analysis reveals ways to solve those challenges and the ethical implications to consider.

A blend of these skills makes up the “three-dimensional data scientist.” There are three key data science competencies that define what Notre Dame calls the three-dimensional data scientist:

  1. Data scientists truly understand the techniques they employ.

    Thanks to their solid foundation in math and statistics, the data science leader has the training to understand what’s going on “under the hood of the car.” They truly understand the techniques they’re using on the models they’re running. When you understand why things work the way they do, you can compare different methods to diagnose problems when they come up, and you can explain your results to any audience. This deep understanding empowers them to not only run lines of code, but to develop nuanced and innovative approaches to complex data questions.

  2. Data scientists are critical thinkers who are trained to act ethically.

    Data scientists understand that it’s about more than math and coding. Data science, and being a data science leader, requires thinking critically and creatively about data and business challenges. Data scientists are agile thinkers, who have a deep understanding of the data science process. When the tools or software change or problems crop-up, they can adapt and problem-solve. They also understand how to analyze the ethical repercussions of their work.

  3. Data scientists can communicate effectively with data.

    Data scientists have strong data visualization skills and the ability to turn data into stories that capture non-technical audiences. Being able to communicate effectively with data means you can push smart business decisions forward.

Becoming a Three-Dimensional Data Scientist

In Notre Dame’s online master’s in data science program, our students become three-dimensional data scientists by going beyond the techniques. Our program is intentionally designed to facilitate deep learning. Students are not just thinking about topics for a couple of weeks. They are thinking about them in a synchronized way across the entire program.

Concurrently, our online master’s in data science also prepares students with a strong ethical foundation so they understand the ethical side of data analysis, as well as excellent communication and presentation skills needed to connect the dots of a sophisticated data analysis for the decision makers in their organizations.

Students in our online master’s in data science program are skilled at asking the right questions and choosing the right problems to solve within their businesses and organizations. This goes back to the premise that companies and organizations are looking to obtain value from data. That’s precisely what we enable our students, the next leaders in data science, to do.

Discover how Notre Dame’s online master’s in data science is preparing the next leaders in data science:

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