How I Got Into The University of Maryland, CP For MS in Data Science?

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Hey! Who are you, what are you currently doing and what is your background?

Hey there! I’m Swattik Maiti, and I’m currently pursuing my Master’s in Data Science at the University of Maryland, College Park. It’s been an exciting journey so far, and looking back, I think the idea of studying abroad and diving deeper into data science had been slowly building up over the years—through projects, work, and just general curiosity. I joined UMD in Fall 2024, and it’s been a mix of intense coursework, cool projects, and gradually figuring out where I want to position myself in this vast field.

Before moving to the US, I did my undergrad in Computer Science at VIT Vellore (2018–2022). VIT had a very dynamic environment, and that’s where I was first introduced to the world of machine learning and data science. I explored quite a few areas—web dev, game dev, competitive coding—but the satisfaction of building something meaningful from raw data stuck with me. 

After graduating, I worked as a Data Scientist at Bajaj Finserv for a bit over two years. The transition from college projects to solving real business problems was both challenging and rewarding. I was mostly involved in credit risk analytics—building models to predict loan defaults, segmenting customers, automating parts of the risk pipeline. It was a mix of working with large datasets, writing production-ready code, and sometimes spending days figuring out why a model’s performance suddenly dropped. But I loved the pace and the impact. It also gave me a reality check on how data science works in practice—lots of iteration, stakeholder alignment, and not every model making it to production. 

How did you decide that you wanted to pursue an MS in the US?

For me, pursuing a master’s wasn’t something I had mapped out years in advance—it was a decision that took shape gradually. While working in the industry, I began to notice the limitations of my knowledge, especially when projects required a deeper, more theoretical understanding of data science. Whenever I was assigned tasks that leaned toward research or involved more complex modeling, I often found myself running into walls. I realized that to truly grow—not just as someone who can build models, but as someone who understands them inside-out—I needed a stronger foundation. That’s when it became clear that doing a master’s wasn’t just a good idea, it was necessary.

As for choosing the US—honestly, it came down to a mix of academic excellence and the sheer range of opportunities. The US is home to some of the top universities in the world, especially for data science and AI, but what really stood out to me was the broader ecosystem. There’s this strong culture of innovation, collaboration, and constant learning that surrounds both academia and industry here

How did you start your application preparation; can you throw some light on profile building, GRE, LORs and SOPs?

My application journey officially kicked off around May 2023, but the idea had been brewing for a while. I wanted to take a structured approach—start early, lock down my GRE score first, and then focus on the rest. My main goal was to wrap up the GRE before August so I’d have enough breathing room to work on my SOP and shortlisting universities. Balancing exam prep with a full-time, 6-day work schedule wasn’t easy, but I built a fairly disciplined routine and stuck to it. In the end, I scored a 321 on the GRE, with a 168 in Quant—which I was quite happy with, considering the time crunch.

After the GRE, I started looking at schools more seriously. I took some help from Yocket’s counseling service to narrow down my list of universities based on my background, goals, and budget. Once I had a tentative list, I shifted my focus to the Statement of Purpose. I genuinely believe the SOP is the most important part of the entire application—it’s the only space where you get to tell your story beyond scores and transcripts. So I didn’t rush it. I spent nearly three weeks refining it, going through multiple drafts, and getting feedback from seniors and friends who had already been through the process.

Around that same time, I also appeared for the IELTS and scored 8.5 out of 9, which helped strengthen the profile further. While test scores and GPAs matter, I really think it’s the personal narrative in the SOP, along with strong letters of recommendation, that make an application stand out. That said—getting those LORs wasn’t a walk in the park. Since I had already graduated and was working full-time, reconnecting with professors took a bit of effort. Some were prompt, others needed a few gentle reminders (and then some more). But eventually, I got what I needed.

I originally aimed to start applications by mid-September, but thanks to the LOR delay, I really got going only by late October. Most of my applications were done and submitted by early December. Funny thing is—UMD, the university I eventually joined, wasn’t even on my initial shortlist! I only applied to it in mid-February after I heard that the program had received STEM designation, which made it more attractive in terms of both content and post-grad opportunities.

What colleges did you apply for and what was the result?

I applied to 9 universities as below.

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Can you tell us more about the course you chose and what the learning outcomes are?

The Master’s in Data Science at UMD is a 30-credit program, consisting of 7 core courses and 3 electives. The core curriculum covers all the essential areas—statistics, machine learning, data analysis, basically most of the deep theoretical topics—while the electives let you dive deeper into specialized topics like deep learning, cloud computing, big data systems, and NLP with a much more hands-on focused approach. 

Most courses are structured as one class per week, typically a three-hour session. So if you’re taking three courses in a semester, that’s just three classes a week—leaving plenty of time for internship applications, self-study, building side projects, or just pursuing other interests. Another thing I really appreciate is that most courses are project-focused rather than exam-heavy. This not only makes learning more practical but also helps you build a solid portfolio of projects that you can directly showcase on your resume—something that really helps during the job hunt.

What is the cost of tuition and cost of living for the program?

One of the big reasons I chose UMD was the relatively affordable tuition. While most universities in the US charge around $50,000–$60,000 (or more) for a two-year master’s program, UMD’s total tuition for the MS in Data Science is roughly around $40,000. That’s a pretty significant difference, especially when you’re factoring in the full cost of studying abroad.

Living in College Park is also fairly reasonable since it’s not a major city. I currently pay $700/month for a shared room—which is on the higher side. If you’re okay with older apartments or living slightly farther from campus, you can definitely find options under $600. My total monthly expenses, including rent, groceries, transport, and misc., stay under $1000. As for scholarships—there aren’t any specific to the MSDS program that I know of. TA positions pay around $20–25/hour and RAs can go up to $30–33/hour, but they’re pretty competitive. That said, there are several non-technical on-campus jobs like working in catering, transport, or the library, which pay around $15–20/hour and are much more accessible.

What does the future after master’s look like?

To be completely honest, the tech job market over the past year or so has been incredibly tough—not just for full-time roles, but even for summer internships. What used to be a reasonably competitive process has now become a full-blown grind, where sending out 600–800 applications for a single internship cycle has become pretty normal. In fact, many people I’ve spoken to in the community say if you’re applying to less than a few hundred roles, you’re likely not even scratching the surface. It sounds crazy, but that’s just how things are right now.

Since I’m still in my first year, I can mostly speak about the internship landscape. That said, I’ve seen both sides of the story—yes, the market is competitive, but there are people managing to break through. At UMD, quite a few folks have landed amazing internships despite the odds. I know at least 4–5 people who got internships at Amazon this year in different roles—ranging from Data Science to Cyber Security to Business Intelligence. There have also been students who landed offers at big names like Meta, Google, Snowflake, and Cloudflare. Personally, I was fortunate to land a summer internship at American Express as a Data Science Intern in their Credit and Fraud Risk team. So while it’s definitely tough, it’s not impossible if you stay consistent and strategic.

Stipends and hourly pay can vary a lot depending on the company and role. From what I’ve seen in my network, hourly pay ranges anywhere from $25/hour to $90/hour. Fintech and big tech firms usually pay at the higher end of the spectrum, while mid-size companies or academic roles tend to be on the lower end—but most of them still pay decently enough to support your living expenses during the internship period. Some companies also offer housing stipends or relocation bonuses, but that really depends on the firm.

However, it’s also important to be realistic—the actual percentage of students who end up landing paid internships is still on the lower side. I’d say the top 15–20% of the class are generally the ones getting internships, with even fewer getting into brand-name companies. Prior experience is a huge differentiator here. People who come in with 2–3 years of relevant work experience in the field tend to have a better shot compared to freshers who only have a couple of short-term internships. That’s not to say freshers don’t get in—it just takes a lot more hustle, networking, and in some cases, luck.

In terms of roles, students from the MS in Data Science and Applied Machine Learning tracks generally apply to internships across a wide range of data-related domains. The most common titles I’ve seen include: Data Science Intern, Machine Learning Engineer Intern, Data Analyst Intern, Data Engineer Intern, BI Analyst Intern, and occasionally more research-heavy roles like Applied Scientist Intern or Research Scientist Intern (though those are few and far between). Some students also explore roles in product analytics or marketing analytics if they have a strong business background.

Any final words of advice for anyone who aspires to be where you currently are?

If you’re planning to come to the US for your master’s, just know that it’s not just an academic journey—it’s a life reset. Moving from India to the US brings a massive shift in your day-to-day life. Suddenly, you’re not only juggling assignments and internship applications, but also doing dishes, cooking meals, grocery runs, laundry, and figuring out how to adult in a completely different system. In the beginning, it feels like a lot—and it is a lot—but trust me, you grow into it. You learn to manage your time, prioritize better, and take pride in the fact that you’re handling everything on your own.

There will be days when the job hunt feels like a black hole—when your applications go unanswered, rejection emails pile up, and it feels like nothing is moving forward. And honestly, those moments suck. But the only thing that works is staying consistent, refining your approach, and reaching out to people. You just have to keep going, especially on the days you don’t feel like it.

One of the most important things I’ve learned here is the value of a good support system. Being away from home, away from everything familiar, can feel isolating at times. But finding people who are on the same journey—your classmates, roommates, even random people you meet at a group project or a study session—makes a world of difference. These are the people who’ll help you cook when you’re swamped, cheer you up after a rejection, and celebrate your wins like their own. So don’t just focus on grades or job applications—invest time in building those connections. Because at the end of the day, it’s not just about surviving this phase—it’s about finding people to share it with, and that’s what makes all the difference.

Resources Suggested by TheGradPost

For GRE/GMAT, especially for acing it in the quant session, try out Target Test Prep (TTP).

Air travel and temporary accommodations – MakeMyTrip

Help with a simpler transactions process – HDFC, SBI, ICICI Credit Cards

Connect

Linkedin – Swattik Maiti

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