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Projects

Water Access Analysis with SQL – Maji Ndogo Dataset (Part 1)

Analyzed real data on water access in Maji Ndogo using SQL. Explored water sources, usage patterns, and data quality issues. Key findings: many people rely on unsafe rivers, shared taps often break or have long wait times, and some records mislabeled contaminated water as clean. Cleaned and validated the dataset to reduce errors and prepare for deeper analysis. This is the first part of a 4-part project that will explore wait times, regional access gaps, and ways to improve water systems with data.

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Water Access Analysis with SQL – Wait Times and Usage Patterns (Part 2)

Explored water usage patterns in Maji Ndogo using SQL, focusing on wait times and demand. Found that households without taps wait an average of 2 hours, with Saturdays being the busiest (~246 minutes) and Sundays the quietest (~82 minutes). Early mornings (6–8 AM) are peak hours, while shared taps remain the most common source. Rural areas account for nearly 60% of water sources, highlighting where support is most needed. This analysis helps identify who needs water, when demand is highest, and where improvements could make the biggest impact.

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Water Quality Auditing with SQL – Accountability and Integrity (Part 3)

Analyzed water quality survey data in Maji Ndogo by comparing field surveyor records with an independent auditor’s report. The goal was to measure accuracy, track employee performance, and uncover potential misconduct. Built queries to compare auditor vs. surveyor scores, flag mismatches, count errors per employee, and analyze qualitative statements for risk signals (e.g., mentions of “cash”)

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Data Cleaning with Python – Coffee Shop Sales Dataset

Cleaned a messy real-world sales dataset using Python and Pandas. Fixed common issues like inconsistent text, placeholder values, missing or invalid data, unparseable dates, and inconsistent column names. Steps included standardizing text, replacing bad values, converting data types, removing duplicates, and renaming columns. This project strengthened my data wrangling skills and set the foundation for deeper analysis and visualizations. click here to see the project

Water Access Planning with SQL – Data Integration and Prioritization (Part 4)

Final part of the Maji Ndogo project focused on merging multiple datasets into a single, actionable table for decision-makers. Integrated visit logs, water source details, pollution tests, and location data to create a clean foundation for analysis.

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Power BI Dashboards for Maji Ndogo Water Project

I also built interactive Power BI dashboards for all four parts of the Maji Ndogo Water Project to bring the SQL analysis to life. The visuals make it easier to see patterns, compare regions, and highlight where the biggest problems are. In Part 1 I focused on water sources and data quality (click here), in Part 2 on wait times and usage patterns (click here), in Part 3 on surveyor performance and accountability (click here), and in Part 4 on repair priorities and population coverage (click here). Together, these dashboards turn raw queries into clear, visual insights for decision-makers.

Sales Dashboard for Sales Managers – Power BI

Built an interactive dashboard to turn raw sales data into actionable insights. It highlights key numbers like total sales, profit, average order value, and discounts, tracks sales trends over time with filters for region, store, or category, and shows top products by revenue and profit. Store performance is visualized with maps and charts, discounts are analyzed with scatter plots, payment methods are broken down by type and average basket size, and category and regional splits reveal opportunities. The flow is intuitive, moving from KPIs to trends, products and stores, discounts, payment methods, and finally category and region insights.

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Customer & Marketing Insights Dashboard – Power BI

Built an interactive dashboard to analyze customer behavior, sales performance, promotions, and product cross-sell opportunities.

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