{"id":50677216,"url":"https://github.com/ash-datapro/reporting-sys","last_synced_at":"2026-06-08T16:05:53.484Z","repository":{"id":361478330,"uuid":"1238322882","full_name":"ash-datapro/reporting-sys","owner":"ash-datapro","description":"Executive reporting system for student success metrics and data-quality 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align=\"center\"\u003eStudent Success Analytics \u0026 Reporting Pipeline\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/R-276DC3?style=flat-square\u0026amp;logo=r\u0026amp;logoColor=white\" alt=\"R\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/SQL-336791?style=flat-square\u0026amp;logo=databricks\u0026amp;logoColor=white\" alt=\"SQL\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/PostgreSQL-4169E1?style=flat-square\u0026amp;logo=postgresql\u0026amp;logoColor=white\" alt=\"PostgreSQL\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Tableau-E97627?style=flat-square\u0026amp;logo=data:image/svg%2Bxml;base64,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\" alt=\"Tableau\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Data_Source-IPEDS-1F4E79?style=flat-square\" alt=\"IPEDS\"\u003e\n\u003c/p\u003e\n\nA 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.\n\nThis 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.\n\n---\n\n## Project Summary\n\nUniversity 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.\n\nThis project builds a repeatable reporting workflow that:\n\n* Processes **six higher-education reporting tables**\n* Covers academic years **2018–19 through 2023–24**\n* Combines and validates **1,177 aggregated reporting rows**\n* Evaluates **12,404 numeric cells** in combined reporting tables\n* Creates reusable SQL reporting views for Tableau\n* Produces an executive dashboard, two data-quality scorecards, a multi-indicator snapshot, and an executive memo\n\n---\n\n## Reporting Outputs at a Glance\n\n| Deliverable                     | Purpose                                                                     |\n| ------------------------------- | --------------------------------------------------------------------------- |\n| Executive dashboard             | Tracks enrollment, retention, and graduation indicators for decision-makers |\n| Higher education data snapshot  | Summarizes national topline trends across six reporting domains             |\n| Combined data quality scorecard | Validates analysis-ready reporting tables                                   |\n| Raw data quality scorecard      | Validates published source workbooks before processing                      |\n| Executive memo                  | Translates findings and data limitations for nontechnical stakeholders      |\n| SQL reporting views             | Supports reproducible KPI and dashboard generation                          |\n| File inventory                  | Documents source-file coverage and reporting inputs                         |\n\n---\n\n## Executive Dashboard\n\nThe 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.\n\n\u003c!-- Replace the path below with the correct relative path to your dashboard image. --\u003e\n\n![Student Success Analytics and Reporting Dashboard](outputs/dashboard/reporting-dashboard.png)\n\n---\n\n## Higher Education Data Snapshot\n\nThe reporting snapshot brings together multiple topline measures to provide a broader view of higher-education trends across the available academic years.\n\n\u003c!-- Replace the path below with the correct relative path to your snapshot image. --\u003e\n\n![Higher Education Data Snapshot](outputs/figures/higher_education_data_snapshot.png)\n\n### Indicators Included\n\n* Retention rate for four-year, full-time students\n* Graduation rate for four-year institutions\n* Undergraduate enrollment\n* Total awards\n* Net price by institutional sector\n* Women’s share of enrollment\n\nThis output demonstrates the ability to consolidate multiple reporting domains into a concise visual summary for stakeholder review.\n\n---\n\n## Data Quality and Reporting Validation\n\nA 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.\n\n### Combined Reporting Tables Quality Scorecard\n\nThe combined scorecard evaluates cleaned and reporting-ready tables after source data have been standardized and prepared for analysis.\n\n\u003c!-- Replace the path below with the correct relative path to your combined quality scorecard image. --\u003e\n\n![Combined Reporting Tables Data Quality Scorecard](outputs/quality/data_quality_scorecard_combined.png)\n\n\n### Raw Published Workbook Quality Scorecard\n\nThe raw-data scorecard evaluates the published source workbooks before they are combined into analysis-ready reporting tables.\n\n\u003c!-- Replace the path below with the correct relative path to your raw quality scorecard image. --\u003e\n\n![Raw Published Workbook Data Quality Scorecard](outputs/quality/data_quality_scorecard_raw.png)\n\n---\n\n## Executive Reporting Brief\n\nThe 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.\n\n\u003c!-- Replace the path below with the correct relative path to your executive memo image. --\u003e\n\n![Institutional Reporting Executive Brief](outputs/reports/executive_memo.png)\n\n### Findings Presented in the Brief\n\n* Processed **1,177 aggregated reporting rows** across six institutional reporting tables covering academic years 2018–19 through 2023–24.\n* Reported **67.6% overall undergraduate retention**, with a **28.7 percentage-point difference** between full-time and part-time enrollment-status groups.\n* Identified a decrease in students reporting no distance education enrollment, from **24.7% in 2019–20** to **18.2% in 2023–24**.\n* Flagged **2 financial-aid records** with missing private nonprofit grant-aid or net-price values and excluded affected measures from metric calculations.\n* Identified the highest graduation-rate missingness among American Indian or Alaska Native reporting measures, with **24 of 240 rate records missing**.\n\n---\n\n## Business Problem\n\nHigher-education reporting offices receive recurring requests about:\n\n* Enrollment trends\n* Student retention\n* Graduation and completion outcomes\n* Financial aid and net price\n* Distance education participation\n* Demographic representation\n* Reporting accuracy and data completeness\n\nWhen reporting processes depend on disconnected files or manual calculations, results can be difficult to reproduce, validate, and communicate consistently.\n\n### Solution\n\nThis project creates a reproducible reporting pipeline that:\n\n1. Inventories public higher-education reporting workbooks.\n2. Cleans and standardizes reporting tables in R.\n3. Validates source completeness, missingness, allowable ranges, and reconciliation logic.\n4. Loads reporting-ready data into PostgreSQL.\n5. Creates reusable SQL views for KPI generation.\n6. Builds a Tableau dashboard and supporting reporting visuals.\n7. Produces an executive brief that documents findings and limitations.\n\n---\n\n## Data Sources\n\nThis project uses publicly reported higher-education data derived from **NCES/IPEDS reporting workbooks and tables**.\n\n### Reporting Domains\n\n| Reporting Table                           | Measures Represented                                |\n| ----------------------------------------- | --------------------------------------------------- |\n| Awards by demographic characteristics     | Credential and award counts by demographic group    |\n| Enrollment by demographic characteristics | Student enrollment counts and demographic reporting |\n| Enrollment by level/status                | Undergraduate totals and enrollment-status measures |\n| Financial aid                             | Grant-aid and net-price indicators                  |\n| Graduation rates                          | Completion-rate reporting measures                  |\n| Undergraduate retention                   | Retention indicators by reported student group      |\n\nThe project uses **aggregated public reporting data**, not individual student records or personally identifiable information.\n\n---\n\n## Technology Stack\n\n| Tool                 | Application                                                                                               |\n| -------------------- | --------------------------------------------------------------------------------------------------------- |\n| **R**                | File inventory, data cleaning, table combination, validation, reporting figures, and scorecard generation |\n| **PostgreSQL / SQL** | Exploratory queries, reusable reporting views, and dashboard-ready KPI tables                             |\n| **Tableau**          | Executive dashboard development                                                                           |\n| **CSV outputs**      | File inventory and processed reporting exports                                                            |\n| **PNG outputs**      | Portfolio-ready scorecards, figures, dashboard previews, and executive memo                               |\n\n---\n\n## Reporting Workflow\n\n```text\nRaw NCES/IPEDS Reporting Workbooks\n              ↓\n      File Inventory and Coverage Review\n              ↓\n        R Cleaning and Standardization\n              ↓\n      Data Quality and Validation Checks\n              ↓\n       Combined Reporting Tables\n              ↓\n       PostgreSQL Reporting Views\n              ↓\n Tableau Dashboard + Reporting Figures\n              ↓\n Executive Memo + Quality Scorecards\n```\n\n### Validation Checks Demonstrated\n\nThe pipeline includes checks for:\n\n* Expected workbook availability\n* Duplicate dataset-year workbooks\n* Unreadable source workbooks\n* Duplicate combined reporting records\n* Numeric missingness and coded missing values\n* Invalid negative numeric values\n* Rates outside the valid 0–100% range\n* Awards demographic reconciliation\n* Enrollment demographic and status reconciliation\n* Financial-aid net-price reconciliation\n* Retention-rate reconciliation\n\n---\n\n## Key Results\n\n| Reporting Metric                             |                  Result |\n| -------------------------------------------- | ----------------------: |\n| Reporting tables processed                   |                       6 |\n| Aggregated reporting rows processed          |                   1,177 |\n| Reporting years covered                      | 2018–19 through 2023–24 |\n| Combined numeric cells evaluated             |                  12,404 |\n| Combined numeric cells coded missing         |                     220 |\n| Combined numeric missingness rate            |                    1.8% |\n| Duplicate combined records identified        |                       0 |\n| Invalid negative values identified           |                       0 |\n| Out-of-range rate values identified          |                       0 |\n| Raw published data cells evaluated           |                  12,164 |\n| Missing expected raw workbooks               |                       0 |\n| Unreadable raw workbooks                     |                       0 |\n| Reconciliation failures in validation checks |                       0 |\n\n---\n\n## Skills Demonstrated\n\nThis project demonstrates experience relevant to institutional research, university analytics, and reporting analyst positions:\n\n* Creating a reproducible reporting workflow across multiple public data sources\n* Cleaning, standardizing, and combining higher-education reporting tables\n* Developing SQL reporting views for dashboard-ready measures\n* Designing automated data-quality and reconciliation checks\n* Identifying missingness and documenting reporting limitations\n* Producing stakeholder-facing dashboards and executive summaries\n* Communicating technical results to nontechnical audiences\n* Organizing code, data, outputs, and documentation for reproducibility\n\n---\n\n## Reporting Limitations\n\n* The project uses aggregated public reporting data rather than student-level administrative records.\n* Reporting availability varies by metric and academic year.\n* Some missing subgroup values were retained as documented data-quality findings and excluded from affected calculations.\n* The dashboard preview presents selected KPIs for an executive overview, while other reporting outputs summarize the broader available reporting window.\n* Results are descriptive reporting indicators and should not be interpreted as causal findings.\n\n---\n\n## Portfolio Takeaway\n\nThis 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:\n\n* Structured source-data management\n* Repeatable cleaning and validation\n* SQL-based reporting logic\n* Executive-facing dashboards\n* Transparent data-quality documentation\n* Clear communication of findings and limitations\n\nThe final deliverables show the full workflow from public reporting files to decision-ready metrics.\n\n---\n\n## License\n\nSee the repository `LICENSE` file for usage terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fash-datapro%2Freporting-sys","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fash-datapro%2Freporting-sys","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fash-datapro%2Freporting-sys/lists"}