Suffolk Launches MS in Data Science

This summer, Suffolk announced the launch of a new Master of Science in Data Science program. Program Director and Mathematics and Computer Science Professor Dmitry Zinoviev explains why the timing is right, and what students will learn about this rapidly-growing and rewarding field: 

Why is this new master’s program in data science launching now?  

In 2015, we started one of the first undergraduate data science courses in Boston. We had good momentum and, following students’ interest in the field, successfully converted it into a graduate program. 

According to the US Bureau of Labor Statistics, data scientist positions are among the fastest-growing jobs in 2024. The projected increase in job openings from 2022 to 2032 is 35%. We can help our students stand out in this growing market by giving them solid technical fundamentals and teaching them how to apply those to diverse industries. 

Why is data science such a booming field?

People love stories, and people love pictures. With a well-designed picture, you can tell your story much more efficiently than with a paper. If you write a report to a customer who is going to make decisions based on your data, the first things they’re going to look at are the conclusion and the pictures. And if they don’t find either compelling, they won’t read the rest of the report. 

The beauty of data science lies in its visual nature. Unlike mathematics, where you get hard numbers or formulas, here you produce charts and graphics as a result of your computation. Data science is a science of educated storytelling. 

What kinds of roles do data scientists play in tech, healthcare, and other industries? 

I bought a car recently. Soon after, I noticed that one of my connections is a data scientist with that car company. Years ago data science wasn’t an obvious or necessary component of the car making business. Now it’s considered essential to most industries.  

Data science is a meta skill. It teaches you how to work with data, but the data comes from various fields. So you can be a data analyst for the media, healthcare or pharmaceuticals, but also in education, transportation, government—virtually any field that produces data or uses data to make decisions.  

Which skills will students learn in the MSDS that will prepare them for this rapidly developing field? 

They’re going to learn advanced programming, mathematical and statistical skills—and also skills in data processing, such as databases, machine learning, and artificial intelligence. Since this is a project-based program, they’ll learn software development while gaining project management and interpersonal communication skills. And, critically, they’ll learn the nuances of presenting data to different audiences, whether that’s to a specialist in a niche industry or to the public via The New York Times.  

We also have a strong focus on ethics in our program, including ethical data collection, ethical data processing, and ethical decision-making based on that data. 

Can you talk a little more about how you prepare your graduates to grapple with ethical issues like data usage and misinformation? 

People tend to trust machine-learning models more than they trust other people, because they assume that computers don’t make mistakes. That’s true to a certain extent. A generative AI, for example, that is trained on facts will produce content based on those facts: data in, data out. But what if it’s trained on lies? Garbage in, garbage out. And some garbage can be toxic. Programs trained on biased and untrue information can create very harmful and false data. 

Once decision makers and the public get false information, it may be too late to say something about the quality of the data. So we have to observe what we are working with and correct it before it’s too late. We also have to ensure that what we do to obtain or analyze data is ethical, respecting privacy, security, and the law.  

Who should choose the MSDS versus a program in business analytics? 

The biggest question is: Do you want to use software developed by others, or do you want to develop programs yourself to customize and control how you collect and analyze data? In business analytics, you’ll learn how to use existing programs at a high level to produce actionable business data. In data science, you’ll be able to tweak and twist and expand and extend and do anything you want within legal and ethical limits. So it really depends on how involved you want to be in controlling the programs you use.  

Since we provide a strong mathematical and statistical foundation, you’ll also be able to understand the quality of your results. And you’ll know how to work with data from many different fields and to present it in ways that are relevant to those decision-makers.  

What kinds of students will thrive in this program? 

 As a data scientist you can make a difference by delivering results, packaging them, visualizing them, providing numerical support, like the values and confidence levels, and delivering a report to leaders who are making important decisions. Students who like working with data, who get fascinated when they see data sets, and who get excited about using tools to transform data sets into interpretable results will find this work rewarding. 

Our program has been designed to welcome students with backgrounds in math and computer science, along with students from fields where data is critically important to their work, such as politics, economics, communications, and the physical sciences. Support from our leveling program will help bridge the technical gap for those who haven’t had advanced training in programming and mathematics. Having students from a wide variety of backgrounds is important because this is such a practical, hands-on program where we work with real data.  

Your own research applies computer science to better understand issues in the humanities and social sciences. Is that type of interdisciplinary problem-solving part of the MSDS? 

In the MSDS, students will take seven courses in math and computer science, followed by three electives from other departments. We structured the program this way so that students develop the skills of working with data first, then apply those skills to real data in different fields such as psychology, biology, or criminal justice.  

The program requires students to take courses outside of their comfort zone. Computer scientists would not normally work with [data from] biology or economics or political science. On the other hand, students who are in the social or behavioral sciences, for example, learn how to apply hard numerical skills to their discipline. I see it as a very valuable cross pollination. 

Your undergraduate program is heavily hands-on and experiential; does that same ethos extend to the MSDS? 

Our philosophy is to have project-oriented classes wherever possible. We do this for a few reasons. We find students master the concepts better when they’re able to apply them to a real-life problem. Working with real data, which is often imperfect, is also the best preparation for your career since you’ll learn how to overcome those challenges.  

Some data science programs are purely process-based, teaching students how to perform, say, a regression while assuming data is readily available and clean, with no missing values, outliers, or typos. But we know that rarely happens in reality.  

At Suffolk, we teach you how to start from scratch. You’ll decide what kind of data is needed to answer the question, where to get this data, and determine the cost of getting the data, not in terms of money, but in terms of programming and time. It may take several months to get the data that you want. And once you get it we want you to be able to clean it up so it can yield useful data. This will help our students as they complete real-world projects in class, pursue data science internships, and launch their careers.