Data Science Languages: R, SPSS, Python, Tableau
Many scientific careers, especially epidemiology, and in academia require students to have a fundamental knowledge of coding and data science for data analysis at the doctorate level. I have conducted a series of both independent and class-based data analysis projects, which explore the intersections of hormonal health, fertility, and reproduction.
Androgen
​
Currently working on creating the first search engine powered for men's hormonal health & androgens. Men's hormonal health and fertility continue to be a topic that remains unaddressed. Reproduction is seen as a "woman's issue when it is both. We aim to empower men as a method of not only assisting women but each couple as a whole optimize their reproductive hormones.
"A Statistical Interpretation of Willingness in Childbearing Amongst Various Age Groups​​" in SPSS
​​​​
This project explores the relationship between socioeconomic factors and fertility intentions across various age groups using data from the Pew Social Trends survey. The analysis focuses on whether financial resources, represented by variables such as income, education, and employment status, are significant predictors of childbearing intentions and how these relationships vary with age.
​​
"Significance of Hormonal Imbalances & Reproductive Disorders About Weight Analysis" in R/ R Studio
​
Conducted an in-depth analysis using the Menstrual Health and PCOS Risk Detection Dataset, focusing on the interplay between demographic, anthropometric, and reproductive variables to assess PCOS risk. Applied advanced data science techniques to explore complex relationships, including the impact of weight on menstrual health indicators such as cycle length and luteal phase. ​
"A Sentiment Analysis on "Hormonal" in Context to Reproductive Health Messaging ​​" in R/ R Studio
​​
​This project aimed to perform a sentiment analysis of the term "hormonal" in the context of reproductive health messaging. By examining how "hormonal" is discussed within this specific domain, we can uncover whether the term carries positive, negative, or neutral sentiments and how it is perceived about reproductive health. The analysis seeks to inform stakeholders in healthcare and communication fields about the language surrounding hormonal health, with potential applications in improving public health messaging and reducing stigma.
​
" Hormones, Diabetes, Blood Pressure Dynamics, and Impacts on Pregnancy" using R/ R Studio
​​
​Conducted in-depth research analyzing a dataset of individuals with Skin Thickness > 50, investigating blood pressure levels in women with 1 and 2 pregnancies. Utilized statistical tests such as permutation and z-tests to compare results, revealing no significant difference. Explored glucose levels and BMI variations in individuals over 20 with different diabetes outcomes, identifying significant differences through statistical analysis.
​​