Prudhvi Devagiri
Data Analyst @Henry Ford Health
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WORK HISTORY
Data Analyst @Henry Ford Health
US
Conducted comprehensive data analysis using SQL, Python, and Excel, leading to data - driven insights that contributed to 80% of successful business strategies and decision- making-Developed interactive dashboards and reports in Tableau that provided stakeholders with actionable insights 90% of the time, empowering data-driven business strategies-Developed SQL scripts to clean and preprocess raw financial data, enhancing data quality and accuracy for subsequent analysis-Ensured compliance with data governance policies and regulatory requirements, maintaining data privacy and security standards throughout analytical projects-Designed and developed ETL (Extract, Transform, Load) mappings and workflows in Informatica, optimizing data processing performance and reducing data loading times-Successfully extracted and transformed large datasets, reducing data processing time by 30%-Transformed disorganized and incomplete data sets into a structured format, ensuring data integrity and accuracy for analysis; improved data quality by implementing data cleansing techniques
EDUCATION
Trine University
Master's degree, Master of science in information studies
ABOUT PRUDHVI DEVAGIRI
Experienced data Analyst with more than 4 years of experience designing and implementing data driven solutions to solve complex business challenges. • Proficient in extracting, cleaning, and analyzing large datasets using Python, SQL, and Excel. Skilled in creating interactive and visually compelling dashboards using Tableau and Power BI to communicate complex data insights effectively. • Experienced in applying statistical techniques to uncover patterns, trends, and correlations within data. Conducted hypothesis testing and predictive modeling to support data-driven decision-making. • Proficient in designing and executing ETL processes to extract, transform, and load data. Skilled in working with relational databases (e.g, MySQL, PostgreSQL) and crafting complex SQL queries. • Proficient in all phases of data mining; data collection, data cleaning, developing models, validation, and visualization. • Developed and maintained data pipelines, improving data accessibility, and reducing processing time by 20%.
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