This project analyzes Walmart’s retail sales data to identify performance patterns, top-selling products, and revenue trends across different locations and time periods.
- Analyze total sales and revenue by branch and category.
- Determine high-performing product lines and customer types.
- Explore seasonal or temporal sales trends.
- Aggregate functions (
SUM,AVG,COUNT) - Grouping and filtering with
GROUP BYandHAVING - Joins for merging multiple tables
- Date and time functions for trend analysis
- Identified the most profitable branches and product lines.
- Discovered sales fluctuations tied to specific months and weekdays.
This project explores India’s startup ecosystem by analyzing data on unicorn companies, funding rounds, and valuations to understand growth patterns across sectors.
- Examine funding trends by industry and year.
- Identify top investors and regions attracting unicorn growth.
- Track valuation changes over time.
- Aggregate and analytical functions
- CTEs for multi-step transformations
- Conditional logic with
CASE WHEN - Subqueries for investor and sector comparisons
- Highlighted industries with the highest funding activity.
- Revealed temporal growth patterns in unicorn valuations.
A data management and analytics project that examines hospital operations, including patient admissions, departments, and treatments, to enhance reporting and efficiency.
- Analyze patient admission rates and departmental workloads.
- Evaluate treatment costs and outcomes.
- Optimize data retrieval through efficient queries.
- Table joins (
INNER,LEFT) - Nested queries for detailed reporting
- String and numeric functions for data cleaning
- Constraints and normalization for data integrity
- Provided clearer visibility into hospital workload distribution.
- Identified cost-intensive treatment categories.
This project investigates real estate property sales to identify pricing trends, influential factors, and regional performance within the housing market.
- Analyze average property prices by region and property type.
- Discover correlations between features (e.g., size, location) and price.
- Detect outliers or undervalued listings.
- Window functions for ranking and averages
- Correlation analysis using subqueries
- Joins across multiple datasets
- Aggregations for summary statistics
- Determined key drivers influencing property pricing.
- Highlighted regions with the strongest sales activity.
This project analyzes customer churn data in the banking sector to identify the main reasons customers leave and suggest strategies for improving retention.
- Identify churn rates by customer segment.
- Analyze correlations between tenure, balance, and churn likelihood.
- Determine high-risk customer profiles.
- Joins and filtering logic
- Conditional statements (
CASE WHEN) - Aggregate functions for churn metrics
- CTEs and subqueries for segmentation
- Uncovered key attributes driving customer attrition.
- Provided actionable insights for retention strategy development.