20 years of AUS course data, beautifully visualized.
An interactive data visualization dashboard exploring every course offered at the American University of Sharjah from 2005 to 2026. 62 fully interactive Plotly charts across 14 analytical sections, a cross-linked Course Explorer (full profiles + visual prerequisite roadmaps), instructor lookup, a Degree Explorer, searchable data tables, and a prerequisite network of 156,512 links — a single static page that runs entirely in your browser, no backend.
Data source: AUSCrawl
62 interactive visualizations across 14 sections, plus 2 interactive explorers:
| Section | Charts | What You'll Find |
|---|---|---|
| University Growth | 2 | Semester-by-semester section count with polynomial trend line, unique courses vs total sections divergence |
| Subject Analysis | 3 | Top 25 subjects by section count, top 10 subject trajectories over time, full subject-by-year heatmap across 20 years |
| Academic Levels | 3 | Undergraduate/Graduate/Doctorate stacked area over time, overall level distribution, graduate percentage by subject |
| Instructor Analysis | 13 | Top 30 most prolific instructors, tenure length distribution, active instructor count over time, teaching diversity scatter (sections vs subjects vs tenure), TBA instructor rate, average workload trends, semester-by-semester recruitment vs departures, retention survival curves by hiring cohort, course ownership scatter (continuity vs turnover), longest instructor-course pairings, plus Team Teaching: co-instruction rate over time, co-teaching rate by department, and the most frequent co-teaching partnerships |
| Teaching Modality | 2 | Traditional vs non-traditional instruction evolution, non-traditional rate by subject |
| COVID-19 Impact | 4 | Section count resilience through the pandemic (with annotated COVID band), instructor and course variety contraction, per-subject indexed heatmap (Fall 2019 = 100), unassigned classroom trend revealing lasting structural shift |
| Schedule Patterns | 5 | Day-time combination heatmap, day pattern popularity distribution, building utilization rankings, Saturday class disappearance (collapsed from 200+ to near-zero by 2010), day pattern evolution stacked area over 20 years |
| Curriculum Evolution | 4 | New courses introduced per year, course longevity distribution, most consistently offered courses across 20 years, discontinued courses timeline |
| Prerequisite Network | 11 | Most connected courses (prerequisites + dependents), interactive COE prerequisite network graph, prerequisite complexity by department, cross-department dependency matrix, top corequisite pairs, corequisite vs prerequisite comparison by department, plus Prerequisite Logic (from the parsed AND/OR boolean trees): requirement shape distribution, logic nesting depth, toughest gateways by mandatory prerequisites, curriculum flexibility by department, and concurrent-enrollment prevalence over time |
| Grade Requirements | 2 | Minimum grade distribution (C- and C dominate), department strictness comparison (stacked percentage) |
| Course Attributes | 4 | Gen-Ed and major attribute tag distribution, attribute category evolution over time, plus Degree Requirement Mapping: programs with the most elective course options, and the most reusable courses across degree programs |
| Course Catalog | 3 | Credit hours distribution, lecture vs lab section ratio with trend, lab and lecture hours breakdown by department |
| Enrollment & Access | 4 | Section closure per semester (open vs closed seats; completed terms only), closure rate by subject, typed include/exclude enrollment restrictions, and the courses most often gated to a specific major/college/program. Note: Banner exposes only a binary open/closed flag, never seat counts — these track section closure, not a true fill rate |
| Browse & Explore | — | Course Explorer — search any course for its full profile (credits, animated sections-per-year chart, usual times, instructors), a visual AND/OR prerequisite roadmap, corequisites, what it unlocks, and which degree programs it counts toward; Instructor Lookup — career-activity timeline, teaching-by-subject breakdown, tenure, and top courses; Degree Explorer — every course tagged toward a major/minor with a subject-composition chart; and searchable course + catalog tables with live filter-responsive summary bars. Everything is cross-linked — one click jumps between courses, instructors, and programs |
Visit the live site at: http://projects.deadpackets.pw/VisualizeAUS/
The site is fully responsive and works on mobile devices.
Or build locally:
git clone https://github.com/DeadPackets/VisualizeAUS
cd VisualizeAUS
# Download the database from the AUSCrawl GitHub Release (gzipped, ~16 MB)
curl -fL -o aus_courses.db.gz \
https://github.com/DeadPackets/AUSCrawl/releases/latest/download/aus_courses.db.gz
gunzip aus_courses.db.gz
# Install dependencies, run the tests, and build
pip install pandas numpy plotly networkx pytest
pytest -q
python build.py
# Open _site/index.html in your browserVisualizeAUS/
build.py # Static site generator (~2,900 lines)
analysis.py # Pure helpers for the prerequisite/restriction/attribute JSON
tests/
test_analysis.py # Unit tests for analysis.py (run with pytest)
aus_courses.db # Downloaded at build time (not in repo)
_site/
index.html # Generated page shell (~0.15 MB — small enough for social crawlers)
data.js # Chart + explorer data payload (~7 MB, loaded by index.html)
.github/
workflows/
deploy.yml # GitHub Pages auto-deployment
The aus_courses.db file contains 11 normalized tables with 75,467 course sections, 1,987 instructors, 3,046 catalog entries, ~73,800 section details, and 156,512 prerequisite/corequisite links — plus section_instructors (every instructor per section, including co-teachers) and catalog_detail (course-level attributes, schedule types, and degree-requirement tags). The dashboard also reads the parsed boolean prerequisite trees (prerequisites_json) for its prerequisite-logic analysis. See AUSCrawl for the full schema documentation.
Built for AUS students, by an AUS student.
AUSCrawl · MIT License