Is an MS in Data Science worth the investment? Two Texas A&M Institute of Data Science experts offer their perspectives.

The Texas A&M University Master of Science in Data Science (MSDS), supported by the Texas A&M Institute of Data Science (TAMIDS), is a valuable investment for anyone seeking an exciting and rewarding career in this growing field. The program has allowed many professionals to upskill quickly and prepare for a variety of career options across industries.

As data science continues to expand beyond traditional technology roles, its applications now span areas such as business, healthcare, engineering, agriculture, and the social sciences. This growing demand means professionals need to be able to work across disciplines and understand how data can be used to address different types of problems. Texas A&M University’s broad academic and research environment gives MSDS students the opportunity to engage with these different perspectives while developing skills they can apply beyond a single industry.

What are the different ways the MSDS prepares students for these opportunities? How can we assess all the factors that make the MSDS a worthwhile investment?

We spoke to two experts at Texas A&M to get their breakdown on what makes the degree worthwhile and the various career paths open to graduates.

Drew Casey, Associate Director for Program Engagement at TAMIDS, brings an industry-facing perspective on strategic partnerships with industry and the value of experiential learning and cross-disciplinary networking. Dr. Scott Crawford, the Director of Master’s Programs at the Department of Statistics and an instructional professor, provides an academic’s insights into statistical modeling, curriculum structure, and data literacy in an AI-driven era.

Drew Casey, Associate Director for Program Engagement

Could you tell us more about your area of specialization and your role at the university?

My background is in education program management, programmatic performance management, and technical communications. I am the Associate Director for Program Engagement at the Texas A&M Institute of Data Science. I lead several of our training and education development programs that provide out-of-the-classroom opportunities. I focus on engagement across university and industry, serving as a bridge between both.

At TAMIDS, we take part in and support diverse, multidisciplinary research labs and initiatives. Our Thematic Labs Program is a collection of TAMIDS-funded labs focused on a singular theme of data science, AI, or machine learning. Examples include the Security, Privacy, and Resilience for Trusted AI (SPARTA) Lab, which develops advanced data-driven cybersecurity and resilience enhancements with an emphasis on critical infrastructures, trustworthy human-AI interactions, and cyber-infrastructure for scientific research. Over at the Data Justice Lab, computational data scientists and social justice-oriented social scientists address the role of data science in social issues across various domains such as health, education, built environments, crisis management, and finance.

Should I pursue a Master’s in Data Science right now given the job market?

Yes, absolutely. Data science remains a resilient field in today’s AI economy. While entry-level coding roles are being reduced, demand for higher-level data analysis is growing. Most organizations have data, but much of it is not AI-ready, and many analysis and management needs still exceed AI’s capabilities.

A master’s in data science provides deeper expertise than an undergraduate degree or on-the-job learning. Companies need data scientists who can turn complex data into more advanced, meaningful insights.

What’s the point of doing a data science degree when you can do computer science instead?

Computer science and data science overlap, but they prepare you for different things. Computer science gives you a strong foundation in computing, software, and systems, while data science focuses more directly on using data to understand problems and support better decisions.

For someone who already knows they want to work with data across different industries, a data science degree is the better option as it provides a focused path. You’re learning technical skills, but you’re also learning how to apply them to real-world problems and communicate insights to people from different fields. The value is really in understanding not just how to build the technology, but how to use data effectively to answer important questions. That can be useful whether you end up working in business, healthcare, engineering, agriculture, or another area.

What makes a Master’s in Data Science a valuable choice for students today?

There are plenty of free resources and learning materials online, and with enough effort, anyone can learn a new skill. A formal degree program from a top-tier research institution gives students access to resources and knowledge they cannot get anywhere else. Data science is tied to other domains, like medicine, geosciences, sports, economics, and every other domain that has data. University is the only place where experts and passionate young professionals frequently mingle and learn from each other.

Also, a master’s program connects you with professors, peers, industry experts, and alumni, opening doors to mentorship, internships, and job opportunities. It provides you with the time to explore and learn new things that will help you throughout your career, including practical skills and new ideas.

Is university reputation important for data science careers?

Yes. Top research universities like Texas A&M equip graduates with analytical and technical skills valued by industry. Hiring data scientists is a significant investment, so employers want confidence in their education and capabilities. A university’s reputation can help provide that assurance. Research-intensive institutions like Texas A&M also offer students opportunities to engage in cutting-edge research, providing experiences that may not be available at smaller universities or in industry settings.

What are the other industries where it is easier — or harder — for data science graduates to transition?

Students often see large technology and software companies like Google or NVIDIA as the go-to companies because they know data is central to their company’s success. But every industry needs data scientists, including aerospace, healthcare, retail, education, and agriculture. Students often struggle to transition into these fields because they need specialized domain knowledge to use data effectively. That is where the degree program’s exposure to these domains becomes invaluable.

Do professors have real industry connections or mostly academic ones?

Most faculty work with industry experts and companies on research projects. Capstone courses, sponsored research, and internships connect academia and industry through faculty and their students. Also, many students keep in contact with faculty after graduating, providing a growing link to new organizations.

What can Master’s in Data Science students do outside the classroom to gain more practical experience?

There are many ways for students to get involved beyond their coursework. We run seminars, workshops, and competitions where students can work with real-world data and problems. Our Student Data Challenge, for example, brings students together to develop solutions using actual datasets. Students can also connect with researchers and industry through capstone projects, research opportunities, and other TAMIDS programs. There are student organizations dedicated to quantum computing, cybersecurity, robotics, AI ethics, and local chapters of professional organizations like the Institute of Electrical and Electronics Engineers (IEEE). The goal is to give students opportunities to apply what they are learning, meet people working in the field, and explore where data science can take them.

How does the university help Master’s in Data Science students find internships and jobs after graduation?

We want students to have support not just while they are earning the degree, but as they prepare for what comes next. Master’s in Data Science students receive access to Interview Query, which gives them interview questions, mock interviews, career guides, and a community with jobs, referrals, and mentors. They can also build connections through TAMIDS events, industry engagement, and capstone projects. Texas A&M also has one of the largest and most passionate alumni organizations in the world, which elevates the university’s career center and resources to help MSDS students connect with industry professionals. These experiences give students opportunities to develop their skills, meet potential employers, and better understand what recruiters are looking for in data science graduates.

Dr. Scott Crawford, Director of Master’s Programs

Could you tell us more about your area of specialization, your role at the university, and the courses or programs you teach? What are your recent research projects and publications?

My areas of specialization include statistical consulting, linear modeling, random forest algorithms, categorical analysis, and predictive modeling.

I serve as the Director of Master’s Programs at the Department of Statistics and as a full instructional professor. My teaching focuses on introductory statistics for students in psychology, engineering, and animal science, as well as secondary-level statistics methodology, categorical statistics, and statistical consulting.

My recent research projects and publications include:

• Examining the effect of population size on genitalia in black soldier flies, using principal component analysis.
• Identifying mosquito species developing resistance to insecticides, using probit regression modeling.
• Examining which types of plastic surgeons are most likely to take on leadership roles, using a looping best-subset model.
• Investigating the advantages of using multiple testing methods in veterinary medicine, based on empirical probability modeling.

My colleagues who teach the MSDS come from the Departments of Computer Science and Engineering (CSE), Electrical and Computer Engineering (ECEN), and Mathematics (MATH).

Should I pursue a Master’s in Data Science right now given the job market?

Yes! The computing power available to companies means data analysis has never been so important or desired. The buzzword is “data-driven decisions”, and while AI can automate the boring parts of an analysis, companies are looking for people who understand what questions need to be asked and how to make a decision powered by statistics but checked with common sense. Our program is focused on the application of technology so you understand what the data does (or does not) say, and then to practice the communication skills to explain the results to people who do not have the technical background but need to make the decisions.

What’s the point of doing a data science degree when you can do computer science instead?

Computer science gives you a strong foundation in computing, but data science focuses on what you can do with the information those systems produce. As AI makes it easier to generate and process data, the ability to interpret that data, question it, and turn it into useful insights becomes increasingly important. A data science education brings together statistics, analytics, and communication, helping students understand not only how to work with data but how to make sense of it. In our program, students also learn alongside peers from different backgrounds, a great way to reflect the reality that data problems often cross disciplines and require different perspectives to solve.

What makes a master’s in data science a valuable choice for students today?

There was a time when a college degree was not essential, but today, a bachelor’s degree may no longer be enough for many high-quality jobs.

A master’s in data science opens many doors in industry, health care, finance, government, manufacturing, and tech companies. Thanks to AI, it is becoming easier for people without formal training to input data and get results. Understanding where those results are limited or wrong, knowing what questions to ask about the analysis used, and adjusting the results to correctly answer the question being asked are more critical than ever.

The surge of misinformation in politics and the media, driven by faulty statistics, clearly shows why we need more people trained to interpret complex data relationships.

Which industry do you work in? Is it viable to only want to work in one industry?

I originally was in finance, but I moved into health science and then engineering based on the coursework I was teaching. By unexpected circumstances, I began doing research with AgriLife, and now a large part of my research has been in that field.

I think a person can specialize in one field if that is where their passion is, and there’s nothing wrong with that mentality. For me, data science has such a broad reach that I encounter people who need data science expertise in all kinds of areas — sociology one day and business the next. There’s hardly a field that doesn’t benefit from data science, and that variety is one of the things I love most about it.

What’s cohort life really like in a data science master’s program?

Students are encouraged to work together in many respects, including homework, projects, and social gatherings. Seminars are a great way to see the research being done by others, and the student socials or data science club meetings offer a fun way to interact in a less formal environment. There is a Discord channel the students use to stay in communication, and many classes have their own discussion boards to encourage collegiality between the students.

Should I choose an online master’s in data science or an on-campus master’s in data science?

The material taught is the same, and the expectations for the assessment, whether an exam, project, or presentation, are the same. The difference lies in the time commitment and social interactions.

The in-class version of a master’s does make it easier to see and interact with other students. It’s easier to stay on task when you’re physically in the building. If you feel more immersed in the student experience, it may be easier to prioritize studying. It does mean you’ll have to fit your work and life schedule around your classes more. Also, many of our students enjoy university life outside the classroom by participating in research projects, competing in data challenges and hackathons, joining student organizations, or taking free tutorials and workshops to learn new skills.

Should the field of data science be more diverse?

Data science is as diverse as the real world. And data science is, by nature, a very diverse field of study, with applications across so many different circumstances that it makes sense for a diverse group of people to be involved. Data scientists come from all sorts of backgrounds and bring a shared passion for understanding.

What can Master’s in Data Science students do outside the classroom?

Students have opportunities to get involved in data science beyond their coursework. They can join student organizations focused on data science and AI, take part in the annual Data Challenge, and use Data2Reality to find research projects on campus. There are also opportunities to apply for scholarships and travel grants to attend data science-related conferences. For graduate students, these experiences can complement their research and capstone work while helping them develop technical, research and professional skills. The goal is to give students different ways to explore data science, build experience, and connect what they learn in the classroom with opportunities across Texas A&M.

How does the university help Data Science students find internships and jobs after graduation?

We give students practical resources to prepare for the job market. MSDS students receive access to Interview Query, where they can practice interview questions, use mock interviews and access career guides, jobs, referrals and mentors. We also connect students with industry through TAMIDS’s Industry Alliance Network. Partners can mentor students, take part in classroom activities, provide project-based learning opportunities and support career development. This gives students opportunities to learn directly from industry professionals, gain practical experience and build connections that can be useful when they start looking for internships or full-time roles.

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