This project simulates the use of AWS-style analytics to perform root cause analysis on student exam registration failures—similar to tasks handled by the College Board's Exam Config team. It demonstrates how to use Python, SQL-style querying, and static visualization techniques to generate insights from operational data.
Educational testing organizations need efficient ways to identify the causes of failed exam registrations and operational bottlenecks.This project simulates an AWS-style analytics workflow to identify registration failure trends using SQL-style analysis and visualization techniques.
- Generate a synthetic dataset of 1,000 exam registrations
- Simulate AWS querying (Athena/Redshift) using SQLite
- Perform root cause analysis on failed registrations
- Visualize insights with a static Plotly bar chart (nbviewer-compatible)
- Export data for simulated S3 integration
- Python Analytics
- SQL & SQLite Querying
- Root Cause Analysis
- Plotly Visualization
- AWS-style Analytics Simulation
- Business Intelligence Reporting
- Data Simulation & Aggregation
- Python (Pandas, NumPy)
- SQLite3 (for SQL-style analytics)
- Plotly (static visualization via Kaleido)
- Google Colab (development environment)
- GitHub + nbviewer (rendered notebook sharing)
👉 ** View in nbviewer**
This version includes:
- Full SQL analysis workflow
- Static Plotly visualization of error codes
- Output-safe format for sharing with reviewers
Alphonso J. Saiewane
Data Scientist | AI Prompt Engineer | International Trade Expert
📧 alphonso.saiewane@gmail.com
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