A weather-analytics and stability engine built specifically to combine QA, API and data-science skills — monitoring and statistically analyzing climate data across 10 global cities.
Overview
Weather Analytics & Stability Engine is a smaller, deliberately cross-disciplinary project — built specifically to combine QA/API testing habits with data-science analysis, rather than to be a large production system. It pulls and analyzes weather data across 10 global cities.
Problem
Most of my QA work and most of my data-science work happen in separate contexts. I wanted one project that genuinely needed both: reliable API consumption and testing on one side, real statistical analysis on the other.
Approach
A Java/Maven project structured around automated, CI-integrated data collection, feeding a statistical layer that computes volatility and standard deviation per city to produce a travel-destination stability recommendation, rather than just reporting raw temperatures.
Engineering
- Weather monitoring across 10 global cities with 100+ data points per city
- A volatility index and standard-deviation-based stability score per city
- Travel-destination stability recommendations derived from the statistical layer, not just raw forecasts
- Automated CI/CD on a Java/Maven structure
Result / Current State
Ongoing. The data-collection and statistical layers are in place; refinement of the stability scoring continues.
What I Learned
Keeping the scope honest — it's a focused engineering exercise combining QA and data-science habits, not an attempt at an enterprise weather platform.
Experimental by design — not the largest project, but a deliberate skills intersection
