
About
Program Overview
Granderson Research Academy is an independent research and data science thesis program for high-achieving high school students who want to engage in college-level research. Students begin by formulating a research question and testable hypothesis, grounded in existing academic literature. They learn how to identify appropriate data sources, define units of analysis, and operationalize variables.
Throughout the program, students work hands-on with real-world datasets, learning data cleaning, exploratory analysis, and statistical reasoning using professional analytical tools. Students conduct descriptive analysis, visualize distributions, and apply inferential methods such as chi-square tests, mean comparisons, and multivariate regression models to evaluate their hypotheses.
Emphasis is placed on interpreting results substantively, understanding limitations, and distinguishing correlation from causal inference.
Each student produces a full thesis-style research paper that includes a literature review, research design, data and methods section, results, and discussion. The program culminates in a formal research conference where students present their findings and defend their methodology. By the end of the program, students leave with a polished, technically rigorous writing sample to attach to college applications that demonstrates advanced critical thinking, quantitative analysis, and the ability to conduct independent scholarly research—skills typically developed only at the college or honors-program level.

Duration & Format
Program Length - 3 Months
Live Instruction
Students attend live Zoom lectures once per week, where core concepts in research design, statistics, and data analysis are introduced.
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Applied Labs
Each week includes live Zoom lab sessions dedicated to hands-on application. During labs, students work directly with data, write and run code (primarily in R), and practice analytical techniques under guidance. Labs emphasize problem-solving, implementation, and technical fluency.
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Weekly Assignments
Students complete weekly homework assignments that reinforce lecture and lab material. Assignments include coding exercises, data analysis tasks, and written components that contribute directly to the student’s final research paper.
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Office Hours
The instructor holds office hours twice per week to provide individualized support and feedback. If a student is unable to attend the designated office hour times, they may contact the instructor via email to arrange alternative support. The instructor will address questions about course material, coding assignments, or research progress, ensuring that all students receive timely, personalized guidance throughout the program.
Meet Our Staff


Pharaoh Granderson, MS Candidate
Primary Instructor
Johns Hopkins, 4.0 GPA
Kwame Granderson, JD
Consultant
Harvard Law School, Cum Laude
Primary Instructor
Pharaoh Granderson, MS Candidate
Pharaoh Granderson holds a strong academic foundation in economics and data analytics, having earned a bachelor’s degree in Managerial Economics and a minor in Accounting, and currently completing a master’s degree in Data Analytics and Policy with a concentration in Statistical Analysis from Johns Hopkins University with a 4.0 grade point average. Her coursework has emphasized probability and statistics, quantitative methods, programming and data management, machine learning, and data visualization. This training has given her a rigorous understanding of how to move from research questions to data-driven results using appropriate statistical and computational tools.
Her research and applied experience center on designing studies, cleaning and managing complex datasets, conducting exploratory and inferential analyses, and communicating these findings. She has worked extensively with real and simulated datasets in R and Python, producing regression analyses, classification models, visualizations, and reproducible research reports using tools such as Quarto and Shiny. Due to her experience developing research questions, testing hypotheses, along with writing and presenting formal research papers, she can mentor students through thesis development—especially in translating ideas into measurable variables, choosing appropriate methods, interpreting results correctly, and presenting findings in clear, well-structured academic writing.
