I am a statistics undergraduate with strong skills in data analysis, statistical modeling, and data visualization. I have proficiency in Python, R, and Excel, and I enjoy handling large datasets, cleaning data, performing regression analyses, ANOVA, and hypothesis testing.
I love turning complex data into actionable insights and communicating them clearly using Tableau and other visualization tools. I am excited to apply and further develop my analytic and technical skills as a data analyst, contributing to data-driven decision making.
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Designed and conducted a cross-sectional study using primary data collected from 377 university
students through structured questionnaires.
Cleaned and analyzed data using R and SPSS, applying Nominal and Ordinal Regression models.
Employed OVERALS (Nonlinear Canonical Correlation Analysis) to uncover complex relationships
between lifestyle, economic capacity, sleep quality, dietary habits, and BMI.
Identified economic capacity and sleep quality as major determinants of dietary behavior and
health outcomes.
Developed comprehensive visualizations using Tableau and authored a full academic research
paper.
I conducted end-to-end market research to identify what drives apartment purchases in a growing city. Applying the Theory of Planned Behavior, I designed a survey, collected data from 131 potential buyers, and performed advanced statistical analysis using SPSS.
The analysis included Factor Analysis and Chi-Square tests to uncover the key psychological and economic factors influencing decisions. The result was a clear, data-driven model highlighting five key drivers like financial accessibility, online reviews, and construction quality.
This model provides real estate developers with actionable insights to refine marketing, improve customer targeting, and make data-backed strategic decisions.
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