I am an international student from India who recently completed an undergraduate degree in Artificial Intelligence and Machine Learning at GITAM, Visakhapatnam, coming straight to my M.S. program at Middle Tennessee State University. Outside of my studies, I am actively involved in data science projects and play cricket in local leagues like the Nashville Cricket League and Tennessee Premier League.
My passion for data science clicked during my undergraduate studies in AI and machine learning at GITAM, where diving into hands-on coursework and practical coding projects showed me how raw data can be transformed into intelligent, real-world solutions. Before starting my degree, I thought data science was mostly just building basic charts and crunching numbers in spreadsheets, but working through actual machine learning workflows completely changed my perspective into seeing it as an exciting, dynamic puzzle of pattern recognition and problem-solving.
I chose MTSU’s data science program because its comprehensive curriculum and practical, hands-on approach stood out right away during the application process. What really sealed the deal for me was how welcoming and supportive the academic environment felt, making it clear that this was the right place to build on my background in AI and machine learning and truly grow as a data professional.
Now in my third semester, my absolute favorite class has been Cases in Data Science under Dr. Rimal, which has also given me the incredible opportunity to work closely with him as a graduate assistant on an exciting project that I am keeping under wraps until it is fully completed. Working directly under his mentorship has completely transformed my hands-on experience and brought everything I have learned into sharp, practical focus.
Five years from now, I hope to be working as a data scientist tackling complex industry problems, with a particular draw toward sports analytics where I can blend my passion for cricket with predictive modeling. If I could share one piece of advice with a prospective student, it would be to dive headfirst into messy, real-world datasets as early as possible because building that practical resilience is where the true learning happens.