CourseMap

Course recommendation engine -- a Python library that recommends courses and learning resources based on user skills, goals, and learning history using content-based filtering.

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Architecture

graph LR
    A[User Profile] -->|skills, goals, level| B[CourseMap Engine]
    C[Course Catalog] -->|topics, level, duration| B
    B -->|content-based filtering| D[Recommendations]
    B -->|topic overlap scoring| E[Learning Paths]
    D --> F[Export JSON / CSV / Markdown]
    E --> F

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classDiagram
    class CourseMap {
        +add_course(title, topics, level, duration)
        +set_profile(skills, goals, level)
        +recommend(profile, n)
        +filter_by_topic(topic)
        +filter_by_level(level)
        +rate_course(course_id, rating)
        +get_popular()
        +get_learning_path(goal)
        +export(recommendations, format)
    }
    class UserProfile {
        +skills: list[str]
        +goals: list[str]
        +level: str
    }
    class Course {
        +id: str
        +title: str
        +topics: list[str]
        +level: str
        +duration: int
        +ratings: list[float]
    }
    CourseMap --> Course
    CourseMap --> UserProfile

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---

Quickstart

Installation

pip install -e .

Usage

from coursemap import CourseMap

engine = CourseMap()

# Add courses to the catalog
engine.add_course("Intro to Python", topics=["python", "programming"], level="beginner", duration=10)
engine.add_course("Machine Learning Basics", topics=["ml", "python", "data-science"], level="intermediate", duration=20)
engine.add_course("Deep Learning with PyTorch", topics=["deep-learning", "pytorch", "ml"], level="advanced", duration=30)
engine.add_course("Data Analysis with Pandas", topics=["pandas", "python", "data-science"], level="beginner", duration=12)

# Set a learner profile
profile = engine.set_profile(
    skills=["python", "statistics"],
    goals=["data-science", "ml"],
    level="intermediate",
)

# Get recommendations
recommendations = engine.recommend(profile, n=3)
for rec in recommendations:
    print(f"{rec.title} (score: {rec.score:.2f})")

# Generate a learning path
path = engine.get_learning_path("ml")
for step in path:
    print(f"  Step: {step.title} [{step.level}]")

# Export results
engine.export(recommendations, format="json")

Running Tests

make test

Linting

make lint

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Configuration

Copy .env.example to .env and adjust settings as needed:

cp .env.example .env

See docs/ARCHITECTURE.md for detailed design documentation.

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Contributing

See CONTRIBUTING.md for guidelines.

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Inspired by AI education recommendation trends

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