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The Granderson Research Academy 

Where Futures are Forged

Original Research & Analysis
 

At Granderson Research Academy, students don’t just learn concepts — they conduct original, data-driven research. With expert mentorship, students design a research question, analyze real-world datasets, and learn how to execute work at a scholarly level. The result is a rigorous, independent research thesis.

Stand-Out in Admissions
 

Top colleges value evidence of intellectual curiosity and independence. A completed research thesis and conference presentation signal maturity, initiative, and academic seriousness far beyond typical extracurriculars. Granderson Research Academy helps students show admissions committees not just who they are — but how they think.

Mission Statement

Our mission is to equip motivated high school students with the skills, mentorship, and intellectual confidence to conduct independent, data-driven research at a college level. Through rigorous analysis, critical thinking, and clear scholarly writing, we prepare students to produce original work that reflects genuine academic depth and sets them apart in competitive admissions and future academic pursuits.

About

Granderson Research Academy is an independent research and data science thesis program for high-achieving high school students seeking genuine, college-level research experience. Students develop an original research question and hypothesis, work with real-world datasets, and apply professional analytical methods to test their ideas. Through hands-on data analysis, statistical modeling, and scholarly writing, students produce a full thesis-style research paper. The program culminates in a formal research conference, and students leave with a polished writing sample that demonstrates advanced critical thinking, quantitative reasoning, and academic independence—qualities that distinguish top college applicants.

Team Analyzing Data

Primary Instructor

Pharaoh Granderson

Master of Science student in Data Analytics and Policy, Concentration in Statistical Analysis, Johns Hopkins University 

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