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Georgia Institute of Technology-Main Campus

Data Science / Statistics · Committee analysis for Zara Okonkwo
Full breakdown →
Admit potential
Medium
Medium confidence
3 support 1 concern

The committee largely agreed that your academic preparation is strong for Georgia Tech — your GPA and SAT sit comfortably within the admitted range. Reviewers were also aligned that your application tells a clear story: using data science to analyze civic systems, highlighted by the police use‑of‑force dashboard and your presentation to Atlanta City Council. Where the discussion became interesting was technical scale. Three reviewers felt the real‑world policy impact made the project compelling, while one argued that Georgia Tech’s strongest admits often build larger engineering systems or conduct deeper technical research. That debate ultimately placed you in the competitive middle of the pool: a credible and coherent data science applicant, but not yet showing the same level of technical infrastructure or research as the most standout admits. Strengthening the technical depth and visibility of your civic‑data work would move you much closer to the top tier.

Committee reads
Academic Reviewer Support
A quantitatively strong civic‑tech builder whose data projects already intersect with public policy in Atlanta.
Watch: Course rigor in advanced math/CS is unknown, and the technical depth of projects is lighter than the most elite GT admits.
Major Gatekeeper Support
A credible civic-data student with real-world impact and strong academics, though lacking the extreme technical scale seen in the very top Georgia Tech applicants.
Watch: Unclear level of advanced mathematical and computational depth relative to the very technical Georgia Tech admit pool.
Fit Reader Support
A civic‑minded data scientist who’s already using Atlanta’s public datasets to ask uncomfortable questions — and would keep doing that at Georgia Tech.
Watch: Technical scale relative to the highest-tier admits — no research lab work, major engineering build, or widely adopted software system yet.
Devil's Advocate Concern
Strong civic data storyteller with real initiative, but I’m not yet convinced the technical ceiling matches Georgia Tech’s top data science admits.
Watch: Whether the underlying technical work behind her projects is sophisticated enough to compete with applicants building advanced systems, research models, or large-scale open-source infrastructure.
▼ Primary blocker
Unclear technical depth relative to Georgia Tech’s top Data Science admits, especially missing evidence of advanced math coursework and large‑scale or research‑level technical builds.
▲ Override condition
Expand the police use‑of‑force project into a technically rigorous data science system — for example releasing a public dataset/API, publishing a statistical or ML analysis with a university mentor, or demonstrating real external adoption (journalists, researchers, civic orgs). Evidence of advanced math coursework would further remove preparation concerns.
Top actions for this school
9
Turn the policing dashboard into a technically robust data science project (publish the dataset, document modeling methods, release code, and show real users such as journalists or nonprofits)
⚙ Medium effort 🕒 before EA or early RD
8
Clearly document advanced math and statistics preparation (AP Calculus BC, AP Statistics, or higher math; include current or planned coursework and any independent study)
⚙ Low effort 🕒 immediately in application materials
7
Quantify open‑source technical work (GitHub repos, commits, stars, contributors, dataset size, algorithms used) so reviewers can see the engineering depth behind the civic projects
⚙ Low effort 🕒 before submitting activities list and additional information section
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