Assignment Management
Back to submission| ID | Name | Rubric | Solution | Criteria | Max Score | Status |
|---|---|---|---|---|---|---|
01-introduction-to-data-science |
Introduction to Data Science | 5 | 100 | Ready | ||
02-introduction-to-python |
Introduction to Python | 6 | 100 | Ready | ||
03-pandas |
Pandas | 7 | 100 | Ready | ||
04-data-visualization |
Data Visualization | 7 | 100 | Ready | ||
05-eda |
Exploratory Data Analysis | 7 | 100 | Ready | ||
06-relational-databases |
Relational Databases | 7 | 100 | Ready | ||
07-introduction-to-machine-learning |
Introduction to Machine Learning | 7 | 100 | Ready | ||
09-ml-classification |
Machine Learning: Classification | 6 | 100 | Ready | ||
11-ml-regression |
Machine Learning: Regression | 6 | 100 | Ready | ||
13-understanding-machine-learning |
Understanding Machine Learning | 5 | 100 | Ready | ||
14-deep-learning |
Deep Learning | 7 | 100 | Ready | ||
15-nlp |
Natural Language Processing | 6 | 100 | Ready |
Expected Directory Layout
assignments/
└── <assignment_id>/
├── rubric.yaml ← required
├── solution.ipynb ← recommended (used as reference by the LLM)
└── answer_key.yaml ← optional
rubric.yaml must contain at minimum an
assignment_name, description, and a list of
criteria each with name,
max_score, and description.