To the Admissions Committee: I taught the applicant Statistics and supervised their analytics project. I recommend them for your MS in Business Analytics.
The applicant built a careful turnout model for our campus elections, complete with confidence intervals — and learned, watching the committee ignore it until they said one plain sentence about moving a booth, that correct analysis is worthless until it becomes a decision. They now design every analysis backward from the decision it must inform. That instinct is the whole point of business analytics.
They are technically sound in R and SQL but distinguished by the last mile — turning a coefficient into an action a non-technical person will take. I recommend them strongly.
To the Admissions Committee: I manage analytics at a D2C brand and supervised the applicant for two years. I support their MS in Business Analytics application.
The applicant built our cohort-LTV model, which reset our customer-acquisition ceiling and stopped us overspending on a channel that only looked good on last-click attribution. But they were the first to tell me our attribution itself was a convenient fiction they understood well enough to distrust — which is exactly why they want the causal-inference training a master's provides.
They run our A/B framework and communicate uncertainty honestly to non-technical leadership. I recommend them with conviction.
To the Admissions Committee: I supervised the applicant on an analytics research project. I recommend them for your MS in Business Analytics.
Analyzing three years of event-ticketing data to explain chronic under-selling, the applicant resisted the tempting narrative and found the real driver was a scheduling conflict no one had modeled. They insisted on validating it against a holdout before believing their own story — the discipline that separates analytics from anecdote.
They document their work for reuse and state the limits of every finding. I endorse them highly.
To the Admissions Committee: I ran a team that consumed the applicant's analytics and recommend them for your MS in Business Analytics as the person who acted on their work.
The applicant's dashboard changed how we scheduled campus events because it made one pattern undeniable — and, crucially, they built it to answer my question, not to show off their modeling. When the data couldn't answer something, they said so rather than manufacturing false confidence. I trusted their numbers because they told me the limits of their numbers.
I recommend them enthusiastically; analytics that changes decisions is rarer than analytics that impresses reviewers, and they do the former.