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Back to school success after 20 years away

Praveen Kumar Myakala (MDataSci’24) earned his master’s in data science while juggling a full-time job and a family. The University of Colorado Boulder alumnus is a graduate of the interdisciplinary Data Science master’s program (MDMS), offered online in partnership with Coursera.

As a non-traditional student, Myakala returned to education nearly two decades after completing his bachelors to take on the program, which provides students with education and training in machine learning, AI tools, data analytics, big data and statistical modeling.

He was able to finish the MDMS in one year.

Why did you decide to go back to school for data science and what led you to choose ÃÛÌÒ´«Ã½Æƽâ°æÏÂÔØ?

There wasn’t a single big moment that triggered the decision, but there was a phase at work that stuck with me. I was architecting systems built around large-scale data processing, prediction pipelines, and KPI scorecards. From an engineering standpoint, I could design and ship them, but I didn’t really understand what was happening under the hood.

That gap kept nagging at me. I didn’t want machine learning to remain a black box in the systems I was building. I wanted to understand why certain models behaved the way they did, what trade-offs I was making, and how design decisions at the data level influenced outcomes.

At first, I explored online courses, but the more I thought about it, I felt I needed something more structured and immersive. While researching programs, ÃÛÌÒ´«Ã½Æƽâ°æÏÂÔØ MS in Data Science stood out immediately. The curriculum was rigorous, current, and thoughtfully designed, and the flexibility of a fully online format made it realistic alongside a full-time job and family life. Just as important, it felt like a program that truly understood and valued non-traditional students coming from industry backgrounds.

What was it like going back to school after being away for so long?

Honestly, the first few months were really humbling. Getting back to structured learning was a process, and there were moments where I had to take a step back and learn how to think like a student again.

But once I got past that initial discomfort, something clicked. Learning actually became exciting.

Topics in data mining, pattern discovery, feature engineering, and working with large messy datasets resonated in a way they never could have earlier in my career. I wasn't simply learning concepts in isolation; I was mapping them all the time to real systems that I had built and problems that I had faced at work.

This actually served to make the experience richer, because being away from school for so long gave me a new perspective, which let me appreciate the deeper learning process more intentionally.

Having a full-time job, a family, and school all at the same time is a balancing act. How did you manage it?

This is without doubt the toughest part. What truly made it possible was support, both at home and at work. My family understood why I was doing this and stood by me throughout the journey.

I spent the greater part of my study time in the evenings when the entire household had gone to bed. I would sit in my home office with a cup of tea and study assignments as well as lectures. The weekends were regularly spent on coursework too.

There were numerous occasions where I decided to dedicate my time to my coursework rather than spending time with my family or friends. It wasn’t an easy task, and there were some compromises made, but having an end goal in sight made all of this easier.

My team at work was also aware that I was pursuing the degree, and their encouragement and flexibility made a big difference.

I didn’t try to do everything at once or pretend it was easy. I stayed consistent, showed up whenever I could, and accepted that steady progress mattered more than getting everything perfect.

What kind of job are you doing now and how does the MS contribute to it?

I work in a senior engineering and leadership role at JPMorgan focused on AI-driven platforms, large-scale systems, and data-intensive decision-making. The MS sharpened how I think. It changed how I evaluate models, question assumptions, and design systems that balance performance, reliability, and responsibility. The degree didn’t replace my industry experience, but it gave me a stronger backbone to support the decisions I make every day. In addition to your job in the private sector, you’re also actively publishing research. What interests you in research and keeps you engaged in that realm?

Industry work moves fast and is usually focused on delivering results within real-world constraints. Research gives me the space to slow down and think more carefully about the kinds of problems that don’t always get that time in day-to-day work. I am especially interested in distributed systems, how large systems continue to work reliably when data and computation are spread across many machines

Publishing research helps me connect what I see in practice with longer-term ideas and lessons. The MS program played an important role in bringing me back to that mindset and reminding me how much I enjoy asking deeper questions, not just building solutions, but understanding them.