π Hello, I'm
A strategic data professional with expertise in Python, SQL, Machine Learning, and Azure Cloud. MS in Data Science from Florida State University. Transforming complex data into actionable insights through advanced analytics.
A strategic data professional with a strong foundation in data science, machine learning, and cloud technologies. Skilled in Python, SQL, Power BI, and Azure, with hands-on experience in automation, dashboard development, and predictive analytics.
Proven ability to translate complex data into actionable insights that drive business performance. Currently based in Jacksonville, Florida, with a Master's degree in Data Science from Florida State University.
MS in Data Science - Computer Science | GPA: 3.6 | Jun 2025
B.Tech in Electronics and Communication | GPA: 3.3 | Apr 2022
Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn, PySpark, PyTorch
Oracle SQL, MySQL, Query Optimization, Database Design, Stored Procedures
Microsoft Certified Associate, Dynamic Dashboards, DAX, Data Modeling
Interactive Dashboards, Data Storytelling, Visual Analytics, Calculated Fields
Data Factory, Synapse Analytics, Data Lake, SQL Database, DevOps
Regression, Classification, EDA, Data Mining, Predictive Analytics
Hypothesis Testing, A/B Testing, Statistical Modeling, R Programming
Data Integration, Pipeline Optimization, Data Transformation, ETL Processes
Published research implementing ML for automated rice quality detection using image processing and classification. Reduced manual inspection time by 30% through Python-based analysis of rice kernel characteristics.
Regression-based model analyzing house prices in U.S. cities, incorporating location, square footage, and amenities. Achieved 90-97% accuracy using Python and advanced statistical techniques with feature selection.
Advanced data wrangling and association analysis for 1M+ customers across shopping malls. Developed predictive models and interactive visualizations enhancing marketing strategies and inventory management.
Analyzed app ratings based on category, content rating, and size. Identified top-performing categories and user preferences using Python data analysis techniques.
UK-based online retail dataset analysis revealing customer purchasing patterns, sales trends, and revenue insights using Python and statistical methods.
Analyzed ticket data to uncover trends in query distribution and optimize resolution efficiency. Delivered actionable insights for improving customer support operations.
Interactive Excel dashboard analyzing sales trends from 1985 to 2016 with regional insights, genre analysis, and platform performance metrics.
Comprehensive Tableau dashboards for employee data visualization and US population income analysis with interactive filters and KPIs.
Complex SQL queries for employee insights and product inventory management. Includes advanced joins, subqueries, and performance optimization.
Jacksonville, FL, US, 32256