Data Science & Analytics Portfolio

Exploratory Data Science & Statistical Modeling

This portfolio entry consolidates multiple specialized data analyses, ranging from classical medical studies to modern consumer trend mappings. Each project focuses on the intersection of data engineering, visualization, and statistical rigour.

Featured Analyses

• Dr. Semmelweis & the Discovery of Handwashing: A historical data-driven investigation using time-series analysis to prove the link between hygiene and mortality rates.
• Google Play Store Analytics: An end-to-end data cleaning and EDA (Exploratory Data Analysis) project on 10,000+ apps to identify market saturation and pricing optimization points.
• Multivariable Linear Regression Modeling: Developed predictive models to analyze the impact of multiple independent variables on target outcomes, focusing on $R^2$ optimization and feature significance.
• Google Trends Visualization: Mapping global search patterns to identify seasonal and long-term interest shifts across various industries.
• Nobel Prize Analysis: EDA and insights from over a century of Nobel Prize data.
• Programming Languages: Trends in programming language popularity over time.
• Computation with NumPy: Array operations and numerical computing exercises.

Technical Proficiency

This collection demonstrates a deep mastery of the Python data stack:

• Data Wrangling: Extensive use of Pandas and NumPy for vectorization, cleaning, and complex transformations.
• Visual Intelligence: Leveraging Seaborn and Matplotlib to build publication-quality statistical visualizations.
• Statistical Rigour: Applying hypothesis testing, p-values, and correlation coefficients to derive actionable insights from raw datasets.

The result is a comprehensive demonstration of how raw data can be transformed into compelling narratives and predictive models.