Master's in Data Analytics with specialized focus on biotech applications, machine learning, and predictive modeling. Bridging the gap between cutting-edge data science and life sciences innovation.
Gained comprehensive knowledge in analytics using R programming, covering data wrangling, exploratory data analysis, statistical modeling, and visualization techniques essential for biotech research.
Collaborated on a real-world project with a clinic in New Jersey, analyzing sleep health data using advanced SQL techniques to explore correlations between sleep patterns and health metrics.
Strengthened statistical foundations including probability distributions, sampling theory, central limit theorem, hypothesis testing, and confidence intervals for robust biostatistical analysis.
Advanced statistical modeling including multiple regression, ANOVA, chi-square testing, and model selection techniques for sophisticated data-driven decision making in research environments.
Team collaboration analyzing Netflix streaming data, creating interactive dashboards and compelling visualizations using Tableau and Python to communicate complex insights to stakeholders effectively.
Extensive use of Microsoft Excel for business analytics, including pivot tables, scenario analysis, and sophisticated data models for enterprise-level decision making in biotech and pharmaceutical industries.
Developed machine learning classification models in Python, including a comprehensive predictive model for diabetes diagnosis using advanced ML algorithms and data preprocessing techniques.
Working with large-scale Yelp datasets, implementing advanced analytics, real-time dashboards, and AI-powered insights for business intelligence and predictive analytics in service industries.
12-week intensive collaboration with Wyman's Blueberries (major US producer) on market analytics and product strategy for new protein blend launch, applying comprehensive business analytics to agricultural biotechnology.
Advanced predictive modeling course focusing on building and evaluating machine learning models. Weekly hands-on projects covering regression techniques, classification algorithms, model selection, and performance optimization for biotech applications.
80% Complete - Expected Graduation: June 2026