Emerging Data Analyst with hands-on experience in data cleaning, analysis, visualization, and business intelligence tools. Skilled in uncovering actionable insights from diverse datasets to drive data-centric decision-ma…
Emerging Data Analyst with hands-on experience in data cleaning, analysis, visualization, and business intelligence tools. Skilled in uncovering actionable insights from diverse datasets to drive data-centric decision-making. Adept at utilizing Python, SQL, and Tableau for real-time analytics and reporting. Seeking opportunities to apply technical expertise to solve complex business challenges.
Gained hands-on experience in collecting, cleaning, analyzing and predicting diverse datasets, including automobile sales, bank customer churn data, aviation data, employee data, hotel bookings, supermarket sales and loan data.
Analyzed 15K+ automotive sales records over 6 years to identify key trends, top manufacturers, and popular vehicle types.
Optimized inventory and marketing strategies by cleaning and imputing missing data using Python’s Pandas library.
Leveraged MySQL to analyze 20K retail customer records, improving retention rates.
Delivered strategic recommendations that improved underperforming staff’s efficiency and increased sales team productivity.
Performed exploratory data analysis on 20K employee salary records across 10 departments and 5 regions to detect pay disparities and turnover trends.
Identified key turnover drivers like workload and compensation, revealing a 15% turnover rate, with 30% of employees citing pay as a primary factor.
Designed dynamic dashboards in Tableau, providing HR with real-time insights into workforce demographics, such as gender ratios (55% male, 45% female) and age distribution (22 to 60 years).
Analyzed six years of bird strike data (25K+ incidents) to identify high-risk locations, vulnerable flight phases, and operational risk factors.
Recommended predictive models and reporting improvements, enhancing aviation safety by 10% and operational efficiency by 8%.
Developed visually compelling dashboards to enable stakeholders to make data-driven decisions.
Analyzed 10K+ loan applications to identify key factors affecting loan approval rates (65%) and customer creditworthiness.
Developed predictive models using Multiple Regression and Logistic Regression on 100K+ financial records, assessing borrower risk and loan default probability.
Achieved an accuracy of 78%, enhancing loan approval decision-making and reducing default risks by 12%.
Processed and cleaned over 5,000+ player statistics and 3,000+ match records using Python and SQL for structured analysis. Engineered features and built a logistic regression model to predict yellow card occurrences with 85% accuracy. Created automated data pipelines to update datasets and refresh dashboards in Tableau. Delivered interactive visualizations analyzing attendance trends, referee patterns, and player performance. Applied data storytelling techniques to communicate insights to non-technical stakeholders.
Analyzed 10K+ loan applications affecting loan approval rates (65%). Used Regression models in Python on 100K+ financial records, achieving 78% accuracy to reduce default risks by 12%.
Analyzed 20K employee salary records across 10 departments using Python to detect pay disparities. Identified turnover drivers, revealing a 15% turnover rate, with 30% citing pay. Designed Tableau dashboards for real-time HR insights (gender ratios: 55% male, 45% female).
Analyzed 15K+ automotive sales records over 6 years using Python’s Pandas and MySQL to identify key trends and top manufacturers. Optimized sales strategies with insights from 20K+ retail customer records, improving staff efficiency and sales team productivity.
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