To the Admissions Committee: I taught the applicant Econometrics and supervised their undergraduate thesis. I recommend them for your MS in Data Science.
Their thesis on agricultural price volatility contained a discovery most students would have buried: the 'weekly average' price column they had trusted for two months was computed inconsistently across states. Rather than quietly proceed, they rebuilt the series from daily records — which changed their headline result — and wrote a candid appendix on how the error had nearly misled them. Treating data provenance as central rather than clerical is the exact instinct data science requires and rarely finds in a fresh graduate.
Technically, they went well beyond the syllabus, teaching themselves enough Python to scrape and reconcile three years of inconsistent state-portal data. But it is their statistical honesty I want to emphasize: they flagged when their sample no longer supported a claim, and revised the claim.
I recommend them without reservation for graduate study in data science.
To the Admissions Committee: I manage the analytics function at a lending startup and have supervised the applicant, a business-intelligence analyst, for two years. I support their MS in Data Science application.
The applicant owns the SQL layer over our data lakehouse and redesigned our transformation jobs to cut reporting latency from daily to hourly. But the moment that convinced me they belong in a rigorous program was a self-imposed correction: they had built a churn model reaching 71% accuracy that leadership began treating as prophecy, and rather than enjoy the credit, they came to me to explain why the model was not trustworthy enough to allocate a retention budget on. An analyst who warns you off their own model is invaluable.
That episode is exactly why they should go back to school. They can call machine-learning methods like APIs; they want to understand the statistics underneath well enough to know when they fail. That is the right reason.
They are rigorous, honest about uncertainty, and ready for graduate work. I recommend them strongly.
To the Admissions Committee: I supervised the applicant on a data-science research assistantship analyzing food-price anomalies. I recommend them for your MS in Data Science.
I assigned the applicant a messy, real problem: detect abnormal price spikes across fragmented agricultural market data. They built a seasonal-decomposition anomaly detector that flagged the 2024 tomato spike two weeks before it made the news — not with an exotic model, but with a well-understood method applied carefully, which is the harder and more valuable skill. They resisted my suggestion to try a fancier approach until they had exhausted what the simple one could explain.
They also documented their pipeline so thoroughly that a later student reproduced the analysis without my help — reproducibility being a research virtue most learn far too late.
They are careful, skeptical of their own results, and unusually resistant to methodological fashion. I recommend them highly for graduate data-science research.
To the Admissions Committee: I led a product team that consumed the applicant's analytics work, and I offer this recommendation for their MS in Data Science as the person on the receiving end of their analysis.
The applicant's cohort-level repayment analysis changed how we priced a loan product — a decision I initially resisted, because it contradicted my intuition. They didn't argue; they built a clear visualization showing exactly which borrower segments my intuition was wrong about, and let the evidence do the persuading. Being proven wrong by a well-constructed chart is a specific pleasure, and they gave it to me more than once.
What made them effective as a partner was restraint: they never oversold a finding, always stated the confidence around it, and told me plainly when the data couldn't answer my question. I trusted their numbers precisely because they told me the limits of their numbers.
I recommend them enthusiastically; a data scientist who communicates uncertainty honestly is worth far more than one who projects false certainty.