Audit Data Quality for a Climate Tech Sensor Network
Overview
What this challenge is about.
Audit Data Quality for a Climate Tech Sensor Network. Intermediate challenge in analysis. Analyzing real datasets and building models that drive decisions, e...
The Brief
What you'll do, and what you'll demonstrate.
Build a data-quality layer that surfaces silent sensor failures and produces a defensible quality report for customer-facing SLAs.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Express data-quality intent declaratively (expectations, contracts)
- Implement basic anomaly detection appropriate to time-series sensor data
- Aggregate quality signals into a customer-facing scorecard
- Translate engineering quality metrics into a sales-grade SLA
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Data Engineering and Big Data Systems
Master · Data Engineering
Strong alignment
This challenge maps to Data Engineering and Big Data Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Data Quality
Apply data quality to solve real industry problems and demonstrate production-level capability.
- Great Expectations
Apply great expectations to solve real industry problems and demonstrate production-level capability.
- Anomaly Detection
Apply anomaly detection to solve real industry problems and demonstrate production-level capability.
- Sql
Query, transform, and analyze relational data to extract meaningful business insights.
- Monitoring
Apply monitoring to solve real industry problems and demonstrate production-level capability.
- Dashboarding
Apply dashboarding to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Engineer
Data-quality monitoring is a top-five responsibility on most senior data-engineer job descriptions; this challenge proves a student can scope and ship one.
This challenge sharpens
- data-quality
- great-expectations
- monitoring
Data Scientist
Anomaly-detection design on real-world time-series with messy edges is daily work for data scientists on operations or trust-and-safety teams.
This challenge sharpens
- anomaly-detection
- sql
- dashboarding
MLOps Engineer
Production data-quality monitoring is increasingly owned by MLOps; the alerting and SLA framing transfers directly.
This challenge sharpens
- monitoring
- data-quality
- anomaly-detection