My Georgia Tech OMSA Year 2 Fall term Takeaway (Fall25)

By Fall 2025, I felt more grounded in OMSA. The self-doubt from my first year hadn’t completely disappeared, but I was more comfortable with the program’s expectations and pace. This term focused heavily on theoretical depth and statistical rigor, and it pushed me to think more carefully about assumptions, modeling choices, and interpretation.

ISYE 6740 – Computational Data Analysis

ISYE 6740 is a mathematically intensive course that dives deep into the foundations of machine learning and data analysis. It draws heavily on calculus, linear algebra, probability, statistics, and optimization, and emphasizes understanding why algorithms work, not just how to apply them. Topics such as dimensionality reduction, clustering, classification, and optimization are treated from a theoretical perspective, often involving careful derivations and proofs.

Prof. Xie is an exceptional instructor—wise, incredibly sharp, and remarkably clear. She has a rare ability to explain mathematically intimidating proofs in a smooth and intuitive way, making “scary” math feel approachable and logical. She is also highly engaged with the course and holds weekly office hours, which go far beyond standard Q&A. During these sessions, she not only answers homework questions but also introduces her new research and emerging technologies, which I found both inspiring and intellectually enriching.

This course demanded sustained focus and patience. It significantly strengthened my intuition across core mathematical foundations and helped me connect abstract theory to practical machine learning methods. Despite the difficulty, CDA became one of my favorite courses in OMSA, precisely because of its depth, clarity, and intellectual rigor. 

No exams—just six proof-based homework assignments with Python coding and one project.


ISYE 6414 – Regression Analysis

ISYE 6414 is a rigorous, statistics-focused course centered on regression modeling and inference. Beyond fitting models, the course emphasizes model assumptions, diagnostics, interpretation, and statistical reasoning, reinforcing the importance of understanding uncertainty, bias, and limitations when drawing conclusions from data.

The course involves substantial R coding. Assessment consists of four analytics homework assignments using R, two midterms, and one group project. The homework focuses on model fitting, diagnostics, hypothesis testing, and interpretation.

The group project goes well beyond coding and follows an academic-style research format, requiring a formal research plan, methodological justification, and a clearly written report that communicates results and limitations. I was very fortunate to work with three amazing teammates, and the collaboration made the project both productive and enjoyable.

Overall, ISYE 6414 sharpened my statistical discipline and strengthened both my analytical rigor and my ability to communicate results clearly in a research-style setting.

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