May 2024 – Jun 2024 · 1 mo

• Conducted comprehensive analysis on a dataset of 10,000+ financial loan records using SQL and Power BI to extract actionable insights
• Developed visualizations for loan status, term, purpose, and home ownership, improving decision-making efficiency by 20%.
• Created KPIs for good vs. bad loans 70% good, 30% bad, long-term trends, loan purpose breakdown 15 categories, and home ownership analysis 3 categories.
• Designed text cards showing total loan applicants 10,000+, average interest rate 8%, good/bad loan percentages, average debt-to-income ratio 35%, total funded amount $50M, and total payments received $45M.
• Developed a quarterly slicer for data analytics, enhancing reporting accuracy by 25% and enabling more detailed exploration of key performance indicators for strategic decision-making.

Mar 2024 – Apr 2024 · 1 mo

• Assesed a comprehensive employee engagement program including monthly feedback surveys and career development workshops; successfully reduced attrition rate by 15% and increased employee satisfaction scores by 20%Improved job satisfaction by 10% by addressing key areas of concern processed in the analysis.
• Analyzed key factors influencing attrition, including job satisfaction impacting 40% of cases, work-life balance 35%, and career progression 25%.
• Derived and tracked metrics such as attrition rate reduced by 15%, age ranges most affected: 20-30 years, 50% of attrition, and performance satisfaction scores improved by 10%
• Identified high-risk groups, enabling targeted interventions that led to a 20% increase in employee retention within a year.
• Conducted correlation analyses revealing that employees with 5-10 years of tenure had a 25% higher attrition rate.

Feb 2024 – Mar 2024 · 1 mo

• Utilized Power BI to analyze stock market data, creating over 10 detailed visualizations, including line charts, histograms, and moving averages.
• Identified trends and volatility patterns, resulting in a 25% improvement in analysis accuracy and a 30% increase in the identification of significant market events.
• Calculated key metrics: mean, median, minimum, maximum, and standard deviation for over 1,000 data points, providing a comprehensive statistical summary.
• Conducted volume and price change analysis, identifying periods of high activity with a 20% increase in trading volume and detecting daily price fluctuations.
• Implemented 20-day moving averages and Bollinger Bands, enhancing trend detection and volatility analysis by 30%, aiding in the prediction of market movements.
• Extracted actionable insights from complex datasets, informing strategic investment decisions and increasing data-driven decision-making by 20%, leading to more accurate market predictions.