This portfolio showcases a collection of analytics, statistical modeling, machine learning, and business intelligence projects demonstrating end-to-end data workflows, from acquisition and preparation through modeling, visualization, and decision support. Each folder represents a key step or methodology, including:
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Data Acquisition & Cleaning: Methods to gather and preprocess raw data for analysis.
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Exploratory Data Analysis (EDA): Initial data investigation techniques to identify patterns, trends, and anomalies.
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Predictive Modeling: Implementation of statistical and machine learning models, such as Linear Regression, Logistic Regression, Naive Bayes, and Random Forests.
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Clustering & Dimensionality Reduction: Insights from unsupervised learning methods like K-Means and Principal Component Analysis.
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Time-Series & Sentiment Analysis: Advanced modeling approaches for sequential data and text-based sentiment extraction.
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Market Basket Analysis: Techniques for understanding associations and consumer behavior.
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Reporting & Communication: Comprehensive reporting to translate technical results into actionable insights.
This portfolio is designed to demonstrate technical proficiency, analytical thinking, and hands-on expertise in solving real-world problems using Python, R, SQL, Tableau, Power BI, and modern data platforms.
While proprietary work cannot be publicly shared, my professional experience includes:
- SQL development and optimization in AWS Redshift and Databricks
- Power BI and Tableau dashboard development
- Enterprise reporting and KPI design
- Data validation and quality assurance
- Financial, operational, and research analytics
- ETL validation and reporting pipeline support
Professional experience spans higher education, research administration, and corporate finance/operations environments.