studentstory

Jayjit Pradhan

Nikitha Konanki Rajeswara Rao — Tue, 07 Apr 2026 15:13:45 +0000

Jayjit Pradhan Nikitha Konank… Tue, 04/07/2026 - 09:13

Can you tell us about your educational and work experience background before you enrolled in this program?

I hold a Bachelor of Engineering in Pharmaceutical Technology from Jadavpur University, India (Class of '07). Over the past 18 years, I have built a career in the IT industry, currently working for Visa Inc. as a Lead Software Engineer.

What initially drew you to this program?

I enrolled in the �Ͼ�Ʒ�� MS-DS program to formalize my data analysis skills and leverage my extensive technical background.

Can you tell us how the MS-DS program fits into your life?

This MS-DS program fits into my life by providing the extreme flexibility needed for a senior IT professional having multiple deliverables. The self-paced, pay-as-you-go course structure allows me to balance a demanding full-time role with academic rigor. Furthermore, the performance-based admission was a key factor for me, as it prioritized my current capability and professional maturity over my 2007 academic records.

What are your favorite parts of the program?

The self-paced, pay-as-you-go course structure and performance-based admission were my favorite structural parts of this program. In terms of curriculum, I really enjoyed the Machine Learning, Text Classification, Big Data courses.

What do you hope to do with your MS-DS degree?

I hope to use my MS-DS degree to apply advanced techniques learned during the course in my work life and create better data-driven solutions for complex financial issues faced.

Would you recommend this program to others? Why or why not?

I would definitely recommend this program to others, as it fits perfectly for a working professional balancing work-life-study.

What do you wish you’d known before starting the MS-DS?

I wish I had searched about it earlier.

What one tip you have for students who are starting this program?

My top tip for incoming students is to prioritize the Statistics Pathway early. As an IT professional, the coding would feel familiar, but the theoretical math requires a different mental muscle.

Is there a specific project you have worked on that stands out to you?

Rather than a single standout project, I found that the entire sequence of projects across the curriculum offered immense value. Coming from a deep IT background, I particularly enjoyed how each project—from initial data cleaning to complex predictive modeling—built upon the last.

Is there anything else you would like to add?

I would just like to add that I have truly loved every bit of this program as of now.

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Ron Sielinski

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 21:08:25 +0000

Ron Sielinski Nikitha Konank… Fri, 03/27/2026 - 15:08

Can you tell us about your educational and work experience background before you enrolled in this program?

I have a BA in English with a math minor, a BS in electrical engineering, and an MFA in creative writing.

What initially drew you to this program?

I've had a long career in the tech industry, including 22 years at Microsoft. Over that time, I've come to appreciate the importance of first principles in data science: understanding its statistical foundations, the intuition behind algorithms, and the motivations that guide their design and use. I began looking for an academic program that would deepen my theoretical knowledge while broadening my technical skills. UC Boulder fully online, accredited program proved to be the perfect fit.

Can you tell us how the MS-DS program fits into your life?

My personal and professional commitments would make it difficult for me to attend classes in a traditional academic setting. Because CU's MS-DS classes are all online--and self-paced--I can fit them into the odd times of day when I'm free: I can listen to lectures on long runs, complete the coursework in the early mornings, and catch up on discussion forums in the evenings.

What are your favorite parts of the program?

My favorite part of the program has been the labs, because they're when I get to apply the concepts that were introduced in the lectures. They let me put theory into practice, deepen my understanding, and experiment with ideas in a hands-on way. And by the time that I finish a lab, I often have a much better grasp of the material and a much greater appreciation of its value, which is especially gratifying.

What do you hope to do with your MS-DS degree?

Data science is a rapidly evolving field—new models, tools, and technologies are constantly emerging—so in many ways we're never finished learning. Fortunately, the MS-DS program has given me the skills and confidence to continue growing with the field. I now feel much more comfortable digging into the technical details of algorithms and working through the formulas in academic papers. In truth, the program has helped me push beyond limits I once placed on myself, and I plan to use my degree as a foundation for continued learning.

Would you recommend this program to others? Why or why not?

CU's MS-DS is a great fit for anyone who's looking for a rigorous program but needs the flexibility of an online learning environment. They'll need to take ownership of their own learning, but if they're self-motivated and disciplined, they'll find the program both challenging and rewarding.

What do you wish you’d known before starting the MS-DS?

If anything, I wish that I had discovered the program sooner.

What one tip you have for students who are starting this program?

Don't presume that the courses will be taught the same way. Each of the instructors brings their own personality and their own pedagogical style to the courses they teach. Embrace those differences—they're part of what makes the program so engaging.

Is there a specific project you have worked on that stands out to you?

I always appreciate when labs give us the freedom to define our own projects. I always try to come up with something I'm truly curious about—a question I genuinely want to answer. For me, that reinforces one of the most practical aspects of the program: the ability to apply what we’ve learned in real-world scenarios. In my case, the questions that I'm curious about often come from my work at IQRush, where we focus on AI visibility. AI--particularly LLMs--represents a huge inflection point for many industries, and one of the exciting things about this space is how many unanswered questions there are. Increasingly, people are using generative search to answer questions and discover information online. Naturally, brands want to know how often their products or websites appear in those answers. Several companies now offer services that attempt to measure AI visibility. Typically, they ask the generative search engines a set of queries and count how often a brand's products or links appear in responses. The results are often presented as if they were deterministic measures of a brand's visibility. In reality, they're only estimates. The whole process contains a substantial amount of variability—from the specific questions being asked to the stochastic nature of LLMs themselves. Ask the same question twice, and you'll get different answers, so if you repeat the same measurement process, you will get different results. But how different? That simple question led to my paper, "Quantifying Uncertainty in AI Visibility," which explores how statistical methods can be used to measure and interpret that variability. Fortunately, we learned how to address problems like these in our coursework. In this case, the Statistical Inference specialization was especially helpful, but I also applied skills from several other specializations: Vital Skills for Data Scientists, Data Mining Foundations and Practice, Data Science Methods for Quality Improvement, and more. I've already posted a preprint of the paper on arXiv, but when I submitted it for publication, I was especially proud to affiliate myself with the �Ͼ�Ʒ��.

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Loi Pham

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 21:05:20 +0000

Loi Pham Nikitha Konank… Fri, 03/27/2026 - 15:05

Can you tell us about your educational and work experience background before you enrolled in this program?

Before enrolling in the MS in Data Science program, I earned a Bachelor of Arts in Mathematics with a minor in Education from the University of California, Santa Barbara. I was a recipient of the Promise Scholars Scholarship, which provided a full ride and supported me as a first-generation, low-income college student. My academic background in mathematics gave me a strong foundation in analytical thinking, problem-solving, and quantitative reasoning. Professionally, I have worked across both education and industry. I previously served as a data analyst at Blizzard Entertainment, where I analyzed engagement and operational data to support business decisions and cross-functional teams. Following that role, I transitioned into education, becoming a lead mathematics teacher and instructional leader in Arizona. In that position, I designed curriculum, led data-driven instructional initiatives, and coached other teachers, which further strengthened my ability to communicate complex data insights to diverse audiences. These experiences ultimately motivated me to pursue advanced training in data science. I wanted to formalize my skills in statistics, machine learning, and data engineering while bridging my background in mathematics, analytics, and real-world problem-solving—goals that aligned closely with the MS-DS program at the �Ͼ�Ʒ��.

What initially drew you to this program?

What initially drew me to the MS in Data Science program at the �Ͼ�Ʒ�� was its strong balance between theoretical rigor and practical, real-world application. Coming from a background in mathematics, education, and industry analytics, I was looking for a program that would deepen my understanding of statistics, algorithms, and machine learning while also emphasizing hands-on projects and applied problem-solving. The fully online format was also a major factor. It allowed me to continue working professionally while pursuing a high-quality graduate education from a respected institution. I was particularly drawn to the program structured curriculum, emphasis on statistical reasoning, and use of industry-relevant tools such as Python, R, and SQL. Ultimately, the program stood out because it aligned closely with my long-term goal of transitioning into advanced data science roles while building a strong, principled foundation in how data is collected, analyzed, and used to drive meaningful decisions.

Can you tell us how the MS-DS program fits into your life?

The MS in Data Science program fits seamlessly into my life by allowing me to balance professional responsibilities, continued learning, and personal growth. As someone who works full-time and has experience across both education and industry, the flexibility of the online format has been essential. It allows me to engage deeply with the coursework while maintaining a demanding schedule. The program has become an integral part of my daily routine. I regularly apply concepts from classes—such as statistical modeling, data analysis, and algorithmic thinking—to real-world problems and ongoing projects. This integration has made the learning experience both practical and immediately relevant, rather than purely academic. Beyond technical skills, the program has also reinforced disciplined thinking and long-term planning in my life. It has helped me structure my time more intentionally, stay focused on continuous improvement, and remain aligned with my broader career goals in data science and analytics.

What are your favorite parts of the program?

One of my favorite parts of the MS in Data Science program is the strong emphasis on building a solid theoretical foundation while consistently applying those ideas to real datasets and real problems. Courses that focus on statistics, probability, and algorithms have helped me think more rigorously about data, rather than relying solely on tools or surface-level techniques. I also appreciate the program project-based approach. Working through structured assignments and analyses has allowed me to build a meaningful portfolio while reinforcing best practices in data cleaning, exploratory analysis, modeling, and interpretation. These experiences have been directly applicable to both industry and research-oriented work. Finally, I value the flexibility and structure of the online format. The curriculum is well organized, expectations are clear, and the pacing encourages steady, disciplined progress—making it possible to engage deeply with the material while balancing professional and personal commitments.

What do you hope to do with your MS-DS degree?

With my MS in Data Science degree, I hope to transition into advanced data science or analytics roles where I can use data to drive impactful, evidence-based decision-making. My goal is to work on complex, real-world problems that require strong statistical reasoning, thoughtful modeling, and clear communication of insights to both technical and non-technical stakeholders. I am particularly interested in roles that sit at the intersection of analytics, product, and strategy—where data can directly influence outcomes and improve systems at scale. The program is equipping me with the technical depth and analytical mindset needed to take on these challenges with confidence. In the long term, I also hope to mentor others entering the field, drawing on my background in education to help bridge the gap between theory and practice and to make data science more accessible and impactful across organizations and communities.

Would you recommend this program to others? Why or why not?

Yes, I would strongly recommend the MS in Data Science program at the �Ͼ�Ʒ��. The program offers a well-balanced curriculum that emphasizes both theoretical foundations and practical application, which is essential for developing long-term competence in data science rather than short-term tool familiarity. The flexibility of the online format makes the program especially accessible for working professionals, while the academic rigor ensures that students are challenged and supported at a high level. The coursework encourages disciplined thinking, strong statistical reasoning, and clear communication—skills that translate directly to real-world data science roles. Overall, the program is a strong fit for individuals who are motivated, self-directed, and looking to build a principled, durable foundation in data science that will serve them throughout their careers.

What do you wish you’d known before starting the MS-DS?

I wish I had known just how much the program emphasizes disciplined, foundational thinking over quick wins or shortcuts. The MS-DS is not just about learning tools or following templates—it requires sustained effort in understanding statistics, probability, algorithms, and how assumptions shape results. That rigor is ultimately a strength of the program, but it something prospective students should be prepared for. I also wish I had better appreciated the importance of consistent time management from the start. Because the program is flexible and self-paced, it rewards students who build strong routines and stay proactive with coursework rather than relying on bursts of last-minute work. That said, these challenges are also what make the program valuable. Knowing this earlier would not have changed my decision, but it would have helped me approach the program with even more intentional structure and confidence from day one.

What one tip you have for students who are starting this program?

Treat the program like a long-term investment rather than a race. Focus on truly understanding the foundations—statistics, probability, and reasoning about data—rather than rushing through assignments or just learning tools at a surface level. If you build consistent study habits early and prioritize depth over speed, the technical skills and confidence will follow naturally, and the program will pay dividends well beyond graduation.

Is there a specific project you have worked on that stands out to you?

One project that stands out to me is a credit card fraud detection program I worked on during the MS in Data Science program. The project focused on identifying fraudulent transactions within highly imbalanced financial datasets, which required careful data preprocessing, feature engineering, and model evaluation. What made this project especially meaningful was the emphasis on selecting appropriate performance metrics and understanding the real-world implications of false positives and false negatives. Rather than optimizing solely for accuracy, I learned to evaluate models using precision, recall, and other metrics that better reflect the costs associated with fraud detection. This project reinforced the importance of combining statistical reasoning with practical decision-making and highlighted how data science can be used to solve problems with tangible financial and consumer impact.

Is there anything else you would like to add?

I’m grateful for the opportunity to be part of a program that values both rigor and real-world relevance. The MS in Data Science program has reinforced my confidence in approaching complex problems thoughtfully and has helped me grow not just technically, but also professionally. I would encourage prospective students to approach the program with curiosity, discipline, and a willingness to engage deeply with the material. The effort you put in is reflected directly in what you gain from the experience, and the skills developed here extend well beyond the classroom.

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Viktoriia Kachanovska

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 20:57:59 +0000

Viktoriia Kachanovska Nikitha Konank… Fri, 03/27/2026 - 14:57

Can you tell us about your educational and work experience background before you enrolled in this program?

I hold a bachelor's degree in applied physics from the Kyiv Polytechnic Institute, which provided me with a deep foundation in mathematical modeling and analytical thinking. Professionally, I spent over seven years building a career in tech, progressing from a test engineer to a lead automation quality assurance engineer. Beyond the corporate world, I also gained some research experience in biophysics at the Bogomoletz Institute of Physiology.

What initially drew you to this program?

Having navigated both academic research and industry leadership, I have always looked for a way to combine my scientific curiosity with my technical expertise. I realized that data science is the common language between the two. Also, I was drawn to �Ͼ�Ʒ�� MS-DS program because of its interdisciplinary curriculum. My background in physics gave me the theoretical tools, and my career in QA gave me the technical discipline; this program offers the perfect environment to merge them.

Can you tell us how the MS-DS program fits into your life?

Currently, I am a stay-at-home mother to two toddlers. I see this period of my life not as a 'break,' but as a strategic pivot point. As I look toward the next chapter of my career, I want to return to the workforce with a modernized, high-impact skill set. The MS-DS program is the perfect fit because of its asynchronous flexibility. It allows me to balance the rigorous demands of a master's degree with the unpredictable schedule of raising young children. Whether I am studying during nap times or late in the evening, I can progress through the curriculum without sacrificing my family needs. This program empowers me to transform my career direction on a timeline that actually works for my life.

What are your favorite parts of the program?

What I appreciate most is the program's structure combined with its flexibility. It focuses on the methodology of data-driven problem-solving. I really enjoy how the courses challenge me to think like a data scientist from day one, how to frame a question, clean messy data, and get actionable insights. The ability to move through these complex topics at my own pace is invaluable and allows me to dive deeper into subjects like machine learning or statistical modeling.

What do you hope to do with your MS-DS degree?

My goal is to transition into a career as a machine learning engineer. I want to leverage my years of experience in quality assurance and apply it to the lifecycle of machine learning models.

Would you recommend this program to others? Why or why not?

I would absolutely recommend this program, particularly to working professionals or parents who require high-level training but have significant time constraints. It provides a top-tier, rigorous master's degree that carries the prestige of �Ͼ�Ʒ��. For anyone looking to deepen their technical expertise, even if they are in the middle of a 'career break' like I am, this program is a great bridge to the next level.

What one tip you have for students who are starting this program?

Discipline is key. Because this program offers so much freedom, you have to be your own most effective manager. My best advice is "the only way to eat an elephant is one piece at a time." Don’t let the complexity of the entire degree intimidate you. Instead, focus on mastering one module, one lab, and one coding challenge at a time. If you stay consistent and keep moving forward, even if it just a little bit every day, the sense of accomplishment at the end is an amazing reward.

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Ikram Ali

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 20:51:28 +0000

Ikram Ali Nikitha Konank… Fri, 03/27/2026 - 14:51

Can you tell us about your educational and work experience background before you enrolled in this program?

Before enrolling, I was already working as a machine learning engineer. Most of my work has been hands-on, building real-world ML systems such as recommendation engines, retrieval/search, and NLP applications. I have worked on end-to-end pipelines: data preparation, training models, evaluation, deployment, and improving performance in production. I joined the MS-DS program to strengthen my academic foundation and make my industry experience even more rigorous.

What initially drew you to this program?

I was drawn to the program because it offers a strong, structured data science curriculum that I could complete online while continuing my full-time job. I wanted deeper theoretical understanding, not just using ML tools, but really understanding why methods work, how to evaluate them properly, and how to make better modeling decisions.

Can you tell us how the MS-DS program fits into your life?

I’m doing the program alongside a full-time role, so it has become part of my weekly routine. I typically study in the evenings and weekends, and I try to connect what I learn directly to my work. The flexibility of the online format makes it possible to keep growing academically without pausing my career.

What are your favorite parts of the program?

My favorite parts are: 1. The structured learning path that strengthens fundamentals (statistics, modeling, evaluation, and practical ML). 2. Assignments that push you to think clearly and explain your work, not just code. 3. The feeling that I am building a strong academic base to complement real industry experience.

What do you hope to do with your MS-DS degree?

With this degree, I want to grow into stronger ML leadership roles where I can design and lead large-scale ML systems end-to-end. I also want to improve my research mindset, communicate technical ideas more clearly, and contribute more through writing, mentoring, and applied research projects.

Would you recommend this program to others? Why or why not?

Yes, I would recommend it especially to working professionals who want a solid academic foundation while staying in their jobs. It a good fit if you’re disciplined, want structured learning, and like connecting theory with practical work.

What do you wish you’d known before starting the MS-DS?

I wish I had known how important time management is from day one. The program is very doable, but it requires consistency. Also, it helps a lot to revise math and statistics basics early, because many topics build on those foundations.

What one tip you have for students who are starting this program?

Treat it like a steady routine, not a last-minute sprint. Study a little every day or every other day, take notes, and do the assignments early. Also, try to connect each topic to a real project or real dataset; it makes learning much easier and more memorable.

Is there a specific project you have worked on that stands out to you?

Yes. One project that stands out is building a large-scale recommendation and retrieval system. It involved designing training pipelines, generating embeddings, evaluating ranking/retrieval quality, and thinking about real production constraints like latency and scaling. It was a great example of how data science connects with engineering and business impact.

Is there anything else you would like to add?

I’m grateful to be part of a program that helps me combine academic depth with real industry work. My goal is to keep growing as both a strong engineer and a more research-informed ML practitioner, and I’m excited to apply what I’m learning to real problems and share that learning with others.

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Heather Fettke

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 20:48:14 +0000

Heather Fettke Nikitha Konank… Fri, 03/27/2026 - 14:48

Can you tell us about your educational and work experience background before you enrolled in this program?

I am pivoting my career a bit. I studied mechanical engineering and atmospheric and oceanic sciences at the University of Maryland. I am currently trying to break into the tech field by working as a software engineer. A part of my undergraduate studies that I really enjoyed was computer vision and ML/AI.

What initially drew you to this program?

I liked the versatility that data science can offer and the focus on machine learning.

Can you tell us how the MS-DS program fits into your life?

The flexibility of this program is perfect. There are no weekly deadlines, which is really helpful. Some weeks I spend 20 hours studying, but others I spend less than 5 if it's a busy week or I have to travel for work.

What do you hope to do with your MS-DS degree?

I hope to pivot my career to get a machine learning role and get promoted.

Would you recommend this program to others? Why or why not?

Yes, I would. It's a very flexible program.

What do you wish you’d known before starting the MS-DS?

The online version does not offer a lot of help with career counseling and other resources that residential students have access to.

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Stephen Walker

Nikitha Konanki Rajeswara Rao — Fri, 27 Mar 2026 20:42:45 +0000

Stephen Walker Nikitha Konank… Fri, 03/27/2026 - 14:42

Can you tell us about your educational and work experience background before you enrolled in this program?

I was the director of data & analytics for a small insurance adjusting company. I design and maintain our SQL database and front-end reports in Excel and Power BI.

What initially drew you to this program?

I liked that the program was self-paced and admissions were simply based on performance in the first few classes.

Can you tell us how the MS-DS program fits into your life?

I was able to complete the program by just putting a couple hours in a day, typically during the weekdays, which was very convenient.

What are your favorite parts of the program?

I liked the interactive SQL database and Python/R notebooks that allowed us to play around with the data first.

What do you hope to do with your MS-DS degree?

I hope to add an AI component to our data department to help predict our clients' volume of claims and losses prior to a storm.

Would you recommend this program to others? Why or why not?

Yes. I believe it is a well-balanced program where you will get out of it what you put into it. It allows for you to take the time to learn the material before moving on.

What do you wish you’d known before starting the MS-DS?

I wish I had known I could complete the core coursework before enrolling in a class.

What one tip you have for students who are starting this program?

I would recommend getting some work experience prior to starting a master's so that you know what subjects you want to learn.

Is there a specific project you have worked on that stands out to you?

I enjoyed projects that allowed us to choose the topic and create a report with a breakdown using text summaries and visuals.

Is there anything else you would like to add?

I wanted to thank the course facilitators for designing a flexible program where working professionals, such as myself, can complete a degree.

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Praveen Kumar Myakala

Nikitha Konanki Rajeswara Rao — Fri, 30 Jan 2026 22:56:27 +0000

Praveen Kumar Myakala Nikitha Konank… Fri, 01/30/2026 - 15:56

Back to school success after 20 years away

Praveen Kumar Myakala (MS-DS graduate ’24) earned his master in data science while juggling a full-time job and a family. The �Ͼ�Ʒ�� alumnus is a graduate of the interdisciplinary Data Science master program (MS-DS), 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 MS-DS in one year.

Why did you decide to go back to school for data science and what led you to choose �Ͼ�Ʒ��?

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, �Ͼ�Ʒ�� 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.

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Solomon Desta

Nikitha Konanki Rajeswara Rao — Tue, 11 Nov 2025 16:57:46 +0000

Solomon Desta Nikitha Konank… Tue, 11/11/2025 - 09:57

What brought you to the MS-DS program?

I hold a bachelor's degree in electrical engineering and have spent over 15 years working in Communication and Electronics Engineering within the federal government. As digital transformation continues to reshape the technological landscape, I felt it was the right time to return to university and deepen my expertise. My professional journey sparked a strong interest in Data Science and Artificial Intelligence. The evolution of digital infrastructure and its growing relevance to my field motivated me to pursue a master degree—not only to expand my technical capabilities but also to contribute more meaningfully to my organization mission and my own long-term development.

How your experience has been so far?

The program has exceeded my expectations. I began with some hesitation, unsure whether I would continue. But as I progressed through the coursework, I found myself increasingly engaged—revisiting foundational concepts, learning new technologies, and gaining confidence with each module. Now that I’ve completed nearly two-thirds of the program, I’m genuinely grateful to be part of it. The faculty, course advisors, and facilitators have been incredibly supportive, and their guidance has played a key role in my success. The learning environment has helped me grow both technically and personally.

What you hope to do with your degree?

My decision to pursue this degree wasn’t driven by immediate career advancement or job seeking. I currently hold a full-time position, which I plan to maintain through the end of my contract in 2026. However, I’m preparing for the possibility of transitioning into a new field once that contract concludes. This program equips me with the skills and flexibility to explore new professional paths, and I see it as a strategic investment in my future—whether that means continuing in public service or moving into a data-driven role in another sector.

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Asuka Saito

CU Data Science — Wed, 16 Jul 2025 13:15:21 +0000

Asuka Saito CU Data Science Wed, 07/16/2025 - 07:15

Can you tell us about your educational and work experience background before you enrolled in this program?

My background was originally on the business side. After university, I did some data analysis work and then pursued an MBA in the UK. When I joined the program, I was at Amazon, working as a program manager/business engineer, analyzing client data and building dashboards for internal programs. At that time, I felt unsure between continuing in business or shifting towards data science. My mentor encouraged me, saying, "It never too late to start new things," so I jumped into data science.

What initially drew you to this program?

I researched a lot. really, —online and offline, Europe and the US—and found Colorado Boulder to be the best fit. Cheaper programs seemed to have weak reputations or curricula, while top-tier ones were often too expensive or required full-time commitment. I wanted to avoid a career gap, so Boulder flexibility was ideal. Its curriculum, blending math, data science, and computer science into practical skills, perfectly matched what I was looking for.

Can you tell us how the MS-DS program fits into your life?

The flexibility was perfect. I could take more courses during slower periods and fewer when busy. Balancing family life and a demanding job became manageable. Also, the courses were highly practical, helping me immediately apply new skills to my work at Amazon.

What are your favorite parts of the program?

Two courses stood out. The Exploratory Data Analysis (EDA) course gave me structured, official processes for data analysis, improving my productivity at work. The Machine Learning course clarified best practices, teaching me to approach modeling more rigorously to achieve reliable results.

I know the ways, but now I have confidence with them.

What do you hope to do with your MS-DS degree?

I graduated in summer 2024, and several exciting changes have happened since then. I’m now a program leader for data science and AI projects at a Japanese company. Although less famous globally than Amazon, the role has better compensation and is more enjoyable.

My manager acknowledges my master's degree as crucial for this new role. Additionally, to my surprise, I became an adjunct professor teaching data science and AI at a university, and I recently received another adjunct professor offer elsewhere. I had not thought I'd teach DS/AI at a university. It is simply amazing.

Would you recommend this program to others? Why or why not?

Absolutely! This program is ideal for those with some practical experience but lacking formal academic training. It great for validating and advancing your skills without sacrificing work, personal interests, or family life—offering an excellent work-life-academic balance.

What's one tip you have for students who are starting this program?

If you’re still deciding, just go for it—I remain grateful to my mentor for pushing me. Once enrolled, take your time and enjoy the process. It's not a race, and there no single right pace. The flexibility to adjust your schedule is the program biggest strength, so relax and trust you'll get there eventually.

Is there a specific project you have worked on that stands out to you?

The Deep Learning course was memorable because it was tough, especially during the winter holiday of 2023 when I dedicated my vacation to it. It was challenging but incredibly rewarding, teaching me more than technical knowledge—it showed me how to handle complex data science projects. The skill still benefits me today.

Is there anything else you would like to add?

I'm truly grateful for the MS-DS program at Colorado Boulder. It has transformed my career, boosted my confidence, and expanded my skills.

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