 Leveraged Python and SQL to clean and analyze large stock market datasets, and addressed issues like missingvalues, transforming raw data into actionable insights for optimized trading strategies and investments. Verified stock market data with financial sources, using SAS for statistical methods, and improved data accuracyby 85% and relevance by 92%, applying KPIs to monitor and enhance data quality for analysis. Built predictive models and statistical analyses that reduced transaction-processing time by 25%, enhancingtrading efficiency and informing strategic investment decisions. Employed AWS S3 for secure storage and Athena for analyzing up to 1TB of complex stock market data,extracting valuable insights to enhance decision-making and operational efficiency. Used Git for version control to enhance transparency in data cleaning, conducted quality checks that improvedefficiency by 30%, and created 15 reports and visualizations in Power BI for stakeholders

 Conducted data cleaning and transformation using Python (Pandas) and SQL, optimizing data quality andreducing processing time through automation, leading to more accurate and efficient analysis. Implemented automated data quality monitoring processes using Python and SQL, resulting in reduction indata discrepancies and ensuring consistent, high-quality data for analytical reporting Developed and maintained automated data pipelines using Apache Airflow, ensuring timely data delivery andreducing manual intervention, which streamlined the reporting process and improved data accuracy. Analyzed trends and customer behavior using advanced statistical techniques, identifying growth opportunitiesand increasing customer retention rates, contributing to overall business growth and improved performance.

 Managed daily healthcare operations for over 500 patient records using EHR systems while optimizingworkflows and streamlining operating room procedures to improve service delivery and accuracy. Analyzed large datasets from EHRs, claims, and patient surveys to develop a chronic disease managementprogram, achieving a 12% increase in patient satisfaction and a 5% reduction in healthcare costs. Optimized reporting with SAS, R, and SQL, boosting patient satisfaction by 15% and reducing response times by10% through patient-focused enhancements and operational improvements. Achieved a 20% improvement in data accuracy through rigorous data query reviews and cleansing of extensivedatasets using Informatica and Talend, ensuring high-integrity data for advanced analysis. Managed administrative tasks and patient coordination for over 200 patients weekly using EHR and PMS,enhancing scheduling, intake, and record management through automated workflows. Established KPIs for patient outcomes and operational efficiency, resulting in a 30% increase in adherence toclinical guidelines and fostering a data-driven culture. Created interactive dashboards and reports in Tableau, cutting report generation time by 30% and enhancingdecision-making for healthcare stakeholders through real-time data visualization