Breaking Games Externship via ExternI build analytics-ready databases and dashboards from messy data using SQL and tools I use with externships.
Projects include SQL database architecture and dashboarding from raw CSVs, applied in an externship with Breaking Games.
Breaking Games · ✅ Verified by Extern · ⏱️ In progress
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
Led analysis of an 800,000-row Chicago crime dataset using Google BigQuery to uncover crime trends and behavioral patterns Built and evaluated predictive models (Logistic Regression, Random Forest, XGBoost) plus an AdaBoost ensemble model, achieving 68% accuracy in predicting crime type with XGBoost Performed statistical analysis with SciPy and geospatial visualization using Matplotlib, Seaborn, and Folium to identify crime hotspots for targeted prevention strategies
I build analytics-ready databases and dashboards from messy data using SQL and tools I use with externships.
I study Data Science at Yeshiva University with a career focus in Data Analytics. I work on SQL and database architecture projects, including an active externship where I turned messy CSVs into an analytics database and a Q4 dashboard.
Externships
SQL & Database Architecture Externship with Breaking Games
In progress
Experience
Data Engineer
MarketOnce · Nov 2024 – Present
Data Science Intern
ZSAnalytics, Inc · May 2024 – Aug 2024
Data Processing Analyst
Toluna India Pvt Ltd · Jun 2017 – Aug 2023
Education
Yeshiva University
Master of Science, Data Analytics and Visualization · Class of 2024
Uttarakhand Technical University
Bachelor of Technology, Computer Science · Class of 2016
Skills
✅ Verified by Extern · ⏱️ In progress
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
The project converted six disparate CSV data sources into a unified analytics database and produced a Q4 dashboard. Work included inventorying datasets, mapping fields across sources, defining schema decisions for analytics queries, and preparing the cleaned tables for dashboarding.
I built a unified SQLite analytics dataset from six CSVs, ran validating SQL queries, and produced immediate answers and cleaned tables used to populate a Q4 dashboard.
On Project 1 I ran SQL queries (with Claude's help) against the Breaking Games SQLite dataset, pasted each query and its results into a shared Google Doc, and reported five instant answers including top products by units and revenue, product count (116), total Meta ad spend ($98,542.56), and 1,197…
Led analysis of an 800,000-row Chicago crime dataset using Google BigQuery to uncover crime trends and behavioral patterns Built and evaluated predictive models (Logistic Regression, Random Forest, XGBoost) plus an AdaBoost ensemble model, achieving 68% accuracy in predicting crime type with XGBoost Performed statistical analysis with SciPy and geospatial visualization using Matplotlib, Seaborn, and Folium to identify crime hotspots for targeted prevention strategies
Analyzed air and water quality trends across New York (2020–2023) by integrating data from multiple public sources Automated data processing and storage into Amazon RDS using AWS Glue for ETL workflows, with AWS SNS configured for real-time email notifications Developed interactive Tableau visualizations to surface trends and support data-driven decision-making
Performed data cleaning and exploratory data analysis using Pandas and NumPy on a lead-conversion dataset Built and tuned a predictive model using a Generalized Linear Model (GLM) with feature scaling and Recursive Feature Elimination (RFE), achieving 88.67% test accuracy Evaluated model performance via confusion matrix, ROC curve, precision, recall, sensitivity, and specificity, improving lead conversion prediction accuracy to 80%