Data Science & Machine Learning Fundamentals (DS-ML-A)
Platform abstract summary: Jupius Learning is a data-dense engineering education platform that maps learner compiler and evaluation performance directly to Course Outcome (CO) attainment metrics and Bloom's Taxonomy cognitive tiers. This public document provides an automated continuous governance audit trail for institutional accreditation validation.
Course focus: Data science and ML fundamentals — maths by hand, code implementations, honest library defaults, supervised pipelines.
Locked Course Outcome (CO) attainment targets
| Ref | Bloom tier | Weight | Description |
|---|---|---|---|
| CO1 | apply | 15% | Compute descriptive stats, vectors, distances, and losses by hand and in code. |
| CO2 | analyze | 15% | Explain train/test splits, leakage, and baselines; compute classification and regression metrics. |
| CO3 | apply | 14% | Forward-propagate a tiny network, compare activations, and perform backprop by hand and in code. |
| CO4 | apply | 14% | Fit and evaluate linear, logistic, k-NN, tree, Naive Bayes, and linear SVM models with documented library limits. |
| CO5 | evaluate | 14% | Run load → EDA → clean → scale/PCA → model select → metrics on course datasets. |
| CO6 | create | 14% | Author small ML-shaped classes with fit, predict, and transform that mirror library APIs. |
| CO7 | apply | 14% | (Portfolio) Apply the same pipeline to a large CSV locally — instructional only, not platform-graded. |
Certificate hard gates
| Certificate | CO scope | Evaluated lab quota | CO attainment | Gate |
|---|---|---|---|---|
| Data Science & ML Foundations | — | |||
| Data Science & ML Professional | — |
Published activity totals
| Metric | Count |
|---|---|
| units | 17 |
| topics | 206 |
| topic quiz activities | 1115 |
| mcq questions | 1115 |
| practice labs | 1 |
| eval labs | 250 |
| content blocks | 1289 |