Data Structures & Algorithms in Python (PY-DS-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 structures and algorithms in Python — class-based ADTs, analysis, and practice with stdlib alternatives.
Locked Course Outcome (CO) attainment targets
| Ref | Bloom tier | Weight | Description |
|---|---|---|---|
| CO1 | analyze | 17% | Analyze algorithm cost (Big-O) and design recursive solutions with clear base/recursive cases. |
| CO2 | apply | 17% | Implement array- and link-based sequence ADTs (list model, stack, queue, deque, linked lists) and compare to stdlib. |
| CO3 | apply | 16% | Build tree and priority-queue/heap structures with traversals and heap operations. |
| CO4 | apply | 17% | Implement maps/hash tables and binary search trees; explain collision and balance trade-offs. |
| CO5 | analyze | 16% | Apply sorting/selection algorithms and light text algorithms; relate to list.sort / bisect. |
| CO6 | evaluate | 17% | Represent graphs, run DFS/BFS and lite shortest-path/MST ideas, and complete integrative capstones. |
Certificate hard gates
| Certificate | CO scope | Evaluated lab quota | CO attainment | Gate |
|---|---|---|---|---|
| Python DS Foundations | CO1–CO2–CO3 | ≥ 90% | ≥ 70% | Python DS Foundations summative quiz |
| Python DS Professional | CO1–CO2–CO3–CO4–CO5–CO6 | ≥ 95% | ≥ 70% | Python DS Professional end-of-course capstone |
Published activity totals
| Metric | Count |
|---|---|
| units | 16 |
| topics | 167 |
| topic quiz activities | 1018 |
| mcq questions | 1018 |
| practice labs | 105 |
| eval labs | 154 |
| content blocks | 983 |