https://github.com/ash-datapro/reporting-sys
Executive reporting system for student success metrics and data-quality validation.
https://github.com/ash-datapro/reporting-sys
data-quality higher-education-analytics r sql student-success tableau
Last synced: 21 days ago
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Executive reporting system for student success metrics and data-quality validation.
- Host: GitHub
- URL: https://github.com/ash-datapro/reporting-sys
- Owner: ash-datapro
- License: mit
- Created: 2026-05-14T02:44:55.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-05-30T19:46:48.000Z (30 days ago)
- Last Synced: 2026-05-30T21:16:58.143Z (30 days ago)
- Topics: data-quality, higher-education-analytics, r, sql, student-success, tableau
- Language: R
- Homepage: https://github.com/ash-datapro/reporting-sys/blob/main/outputs/dashboard/reporting-dashboard.png
- Size: 1.82 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Student Success Analytics & Reporting Pipeline
A reproducible higher-education reporting system built with **R, PostgreSQL/SQL, and Tableau** to transform public NCES/IPEDS reporting data into validated metrics, executive-facing dashboards, quality-assurance scorecards, and a stakeholder-ready reporting brief.
This project was designed to reflect institutional research and university data analyst work: combining multiple reporting tables, validating published measures, documenting data-quality limitations, creating reusable SQL reporting views, and communicating usable metrics to nontechnical decision-makers.
---
## Project Summary
University reporting teams regularly answer questions about enrollment, retention, graduation, student access, financial aid, and program outcomes. These questions require more than visualizations: analysts must organize multiple source files, validate reported values, document limitations, define metrics consistently, and deliver outputs that leaders can interpret quickly.
This project builds a repeatable reporting workflow that:
* Processes **six higher-education reporting tables**
* Covers academic years **2018–19 through 2023–24**
* Combines and validates **1,177 aggregated reporting rows**
* Evaluates **12,404 numeric cells** in combined reporting tables
* Creates reusable SQL reporting views for Tableau
* Produces an executive dashboard, two data-quality scorecards, a multi-indicator snapshot, and an executive memo
---
## Reporting Outputs at a Glance
| Deliverable | Purpose |
| ------------------------------- | --------------------------------------------------------------------------- |
| Executive dashboard | Tracks enrollment, retention, and graduation indicators for decision-makers |
| Higher education data snapshot | Summarizes national topline trends across six reporting domains |
| Combined data quality scorecard | Validates analysis-ready reporting tables |
| Raw data quality scorecard | Validates published source workbooks before processing |
| Executive memo | Translates findings and data limitations for nontechnical stakeholders |
| SQL reporting views | Supports reproducible KPI and dashboard generation |
| File inventory | Documents source-file coverage and reporting inputs |
---
## Executive Dashboard
The Tableau dashboard provides a high-level view of enrollment, retention, and graduation trends. It is designed as an executive overview for institutional leaders who need a fast summary of major performance indicators and multi-year change.
---
## Higher Education Data Snapshot
The reporting snapshot brings together multiple topline measures to provide a broader view of higher-education trends across the available academic years.
### Indicators Included
* Retention rate for four-year, full-time students
* Graduation rate for four-year institutions
* Undergraduate enrollment
* Total awards
* Net price by institutional sector
* Women’s share of enrollment
This output demonstrates the ability to consolidate multiple reporting domains into a concise visual summary for stakeholder review.
---
## Data Quality and Reporting Validation
A central feature of this project is the data-quality layer. Rather than presenting metrics without context, the workflow evaluates source availability, duplicates, missing values, invalid ranges, and reconciliation logic before producing reporting outputs.
### Combined Reporting Tables Quality Scorecard
The combined scorecard evaluates cleaned and reporting-ready tables after source data have been standardized and prepared for analysis.
### Raw Published Workbook Quality Scorecard
The raw-data scorecard evaluates the published source workbooks before they are combined into analysis-ready reporting tables.
---
## Executive Reporting Brief
The project includes an executive memo that converts technical reporting outputs into a concise briefing for institutional decision-makers. The memo presents headline measures, key findings, and data-quality cautions in a format designed for nontechnical review.
### Findings Presented in the Brief
* Processed **1,177 aggregated reporting rows** across six institutional reporting tables covering academic years 2018–19 through 2023–24.
* Reported **67.6% overall undergraduate retention**, with a **28.7 percentage-point difference** between full-time and part-time enrollment-status groups.
* Identified a decrease in students reporting no distance education enrollment, from **24.7% in 2019–20** to **18.2% in 2023–24**.
* Flagged **2 financial-aid records** with missing private nonprofit grant-aid or net-price values and excluded affected measures from metric calculations.
* Identified the highest graduation-rate missingness among American Indian or Alaska Native reporting measures, with **24 of 240 rate records missing**.
---
## Business Problem
Higher-education reporting offices receive recurring requests about:
* Enrollment trends
* Student retention
* Graduation and completion outcomes
* Financial aid and net price
* Distance education participation
* Demographic representation
* Reporting accuracy and data completeness
When reporting processes depend on disconnected files or manual calculations, results can be difficult to reproduce, validate, and communicate consistently.
### Solution
This project creates a reproducible reporting pipeline that:
1. Inventories public higher-education reporting workbooks.
2. Cleans and standardizes reporting tables in R.
3. Validates source completeness, missingness, allowable ranges, and reconciliation logic.
4. Loads reporting-ready data into PostgreSQL.
5. Creates reusable SQL views for KPI generation.
6. Builds a Tableau dashboard and supporting reporting visuals.
7. Produces an executive brief that documents findings and limitations.
---
## Data Sources
This project uses publicly reported higher-education data derived from **NCES/IPEDS reporting workbooks and tables**.
### Reporting Domains
| Reporting Table | Measures Represented |
| ----------------------------------------- | --------------------------------------------------- |
| Awards by demographic characteristics | Credential and award counts by demographic group |
| Enrollment by demographic characteristics | Student enrollment counts and demographic reporting |
| Enrollment by level/status | Undergraduate totals and enrollment-status measures |
| Financial aid | Grant-aid and net-price indicators |
| Graduation rates | Completion-rate reporting measures |
| Undergraduate retention | Retention indicators by reported student group |
The project uses **aggregated public reporting data**, not individual student records or personally identifiable information.
---
## Technology Stack
| Tool | Application |
| -------------------- | --------------------------------------------------------------------------------------------------------- |
| **R** | File inventory, data cleaning, table combination, validation, reporting figures, and scorecard generation |
| **PostgreSQL / SQL** | Exploratory queries, reusable reporting views, and dashboard-ready KPI tables |
| **Tableau** | Executive dashboard development |
| **CSV outputs** | File inventory and processed reporting exports |
| **PNG outputs** | Portfolio-ready scorecards, figures, dashboard previews, and executive memo |
---
## Reporting Workflow
```text
Raw NCES/IPEDS Reporting Workbooks
↓
File Inventory and Coverage Review
↓
R Cleaning and Standardization
↓
Data Quality and Validation Checks
↓
Combined Reporting Tables
↓
PostgreSQL Reporting Views
↓
Tableau Dashboard + Reporting Figures
↓
Executive Memo + Quality Scorecards
```
### Validation Checks Demonstrated
The pipeline includes checks for:
* Expected workbook availability
* Duplicate dataset-year workbooks
* Unreadable source workbooks
* Duplicate combined reporting records
* Numeric missingness and coded missing values
* Invalid negative numeric values
* Rates outside the valid 0–100% range
* Awards demographic reconciliation
* Enrollment demographic and status reconciliation
* Financial-aid net-price reconciliation
* Retention-rate reconciliation
---
## Key Results
| Reporting Metric | Result |
| -------------------------------------------- | ----------------------: |
| Reporting tables processed | 6 |
| Aggregated reporting rows processed | 1,177 |
| Reporting years covered | 2018–19 through 2023–24 |
| Combined numeric cells evaluated | 12,404 |
| Combined numeric cells coded missing | 220 |
| Combined numeric missingness rate | 1.8% |
| Duplicate combined records identified | 0 |
| Invalid negative values identified | 0 |
| Out-of-range rate values identified | 0 |
| Raw published data cells evaluated | 12,164 |
| Missing expected raw workbooks | 0 |
| Unreadable raw workbooks | 0 |
| Reconciliation failures in validation checks | 0 |
---
## Skills Demonstrated
This project demonstrates experience relevant to institutional research, university analytics, and reporting analyst positions:
* Creating a reproducible reporting workflow across multiple public data sources
* Cleaning, standardizing, and combining higher-education reporting tables
* Developing SQL reporting views for dashboard-ready measures
* Designing automated data-quality and reconciliation checks
* Identifying missingness and documenting reporting limitations
* Producing stakeholder-facing dashboards and executive summaries
* Communicating technical results to nontechnical audiences
* Organizing code, data, outputs, and documentation for reproducibility
---
## Reporting Limitations
* The project uses aggregated public reporting data rather than student-level administrative records.
* Reporting availability varies by metric and academic year.
* Some missing subgroup values were retained as documented data-quality findings and excluded from affected calculations.
* The dashboard preview presents selected KPIs for an executive overview, while other reporting outputs summarize the broader available reporting window.
* Results are descriptive reporting indicators and should not be interpreted as causal findings.
---
## Portfolio Takeaway
This project is not only an analysis of higher-education data. It is a small reporting system designed to demonstrate how recurring institutional questions can be answered through:
* Structured source-data management
* Repeatable cleaning and validation
* SQL-based reporting logic
* Executive-facing dashboards
* Transparent data-quality documentation
* Clear communication of findings and limitations
The final deliverables show the full workflow from public reporting files to decision-ready metrics.
---
## License
See the repository `LICENSE` file for usage terms.