Generate EdTech Synthetic Data in Python

EdTech platforms have a distinctive data model: instructors create courses, students enroll and progress at non-uniform rates, quiz scores cluster toward passing (most students who attempt pass), and enrollment spikes around the new year and back-to-school season. Misata generates a five-table EdTech dataset with all of this built in, instructors, courses, students, enrollments, and quiz attempts, with no orphaned foreign keys and realistic completion and certificate issuance rates.

import misata

tables = misata.generate(
    "An edtech platform with 5k students and 200 courses",
    rows=5000,
    seed=42,
)
print(list(tables.keys()))   # ['instructors', 'courses', 'students', 'enrollments', 'quiz_attempts']
print(tables["enrollments"][["completion_pct", "certificate_issued"]].describe())

What Misata generates

Five tables: instructors → courses → enrollments (linking students to courses) → quiz_attempts. Every enrollment references a real student and course; every quiz attempt references a real enrollment.

Tables and columns

TableKey columns
instructorsinstructor_id, name, email, specialty, rating, courses_taught
coursescourse_id, instructor_id, title, category, price, difficulty, duration_hours, rating
studentsstudent_id, name, email, country, joined_at, total_courses_enrolled
enrollmentsenrollment_id, student_id, course_id, enrolled_at, completion_pct, completed_at, certificate_issued
quiz_attemptsattempt_id, enrollment_id, quiz_name, score, passed, attempted_at

Realistic distributions

  • Enrollment timing peaks in January (New Year learning resolutions) and August-September (back to school)
  • Completion rates follow a realistic dropout curve: ~30% of enrollments reach 100% completion
  • certificate_issued is only true for completed enrollments, conditional logic enforced
  • Quiz scores cluster toward passing (60–85 range) with a realistic fail tail
  • Course prices lognormal, free courses plus premium paid content

Quick start

import misata

tables = misata.generate(
    "An edtech platform with 5k students — back to school surge, New Year spike in programming courses",
    rows=5000,
    seed=42,
)

# Enrollment seasonality
import pandas as pd
enrollments = tables["enrollments"].copy()
enrollments["month"] = pd.to_datetime(enrollments["enrolled_at"]).dt.month
print(enrollments.groupby("month").size())   # spikes in Jan and Sep

# Certificate issuance rate
cert_rate = tables["enrollments"]["certificate_issued"].mean()
print(f"Certificate issuance rate: {cert_rate:.1%}")

# Quiz pass rate by difficulty
merged = tables["quiz_attempts"].merge(
    tables["enrollments"].merge(tables["courses"][["course_id", "difficulty"]], on="course_id"),
    on="enrollment_id"
)
print(merged.groupby("difficulty")["passed"].mean())

Common use cases

  • LMS platform development: seed a test database with instructors, courses, and enrollment histories before your platform has real users
  • Learning analytics dashboards: build completion rate, dropout analysis, and learner progress reports on realistic enrollment data
  • Recommendation engine training: use enrollment and quiz score histories to prototype course recommendation models
  • Revenue and subscription analytics: test cohort revenue, course monetization, and refund rate calculations before real sales data exists
  • Adaptive learning system testing: validate quiz difficulty adaptation logic against thousands of attempts with varied score distributions
  • GDPR-safe student data exports: replace real student records with synthetic equivalents for vendor integrations and compliance audits

Advanced: enrollment narrative curves

tables = misata.generate(
    "Edtech platform with 10k students — January New Year resolution spike, "
    "August back-to-school surge, summer lull in June-July",
    rows=10_000,
    seed=42,
)

Advanced: locale-aware generation

# Indian edtech — Indian student names, INR course pricing, regional subjects
tables = misata.generate("Indian edtech platform with 5k students and coding courses", rows=5000)

# Global MOOC — students from US, UK, India, Nigeria, Brazil
tables = misata.generate("Global online learning platform with 10k students", rows=10_000)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "EdTech platform with 5k students",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds completion_pct↔quiz score correlations
    rows=5000,
    seed=42,
)

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