Portable learning
publiccourseMCF 1.1

Data Structures and Algorithms 0.2.0

Published by @apv on August 12, 2026.

Source checksum
48f2032bfe31aea490ec9d10c526f3351505632cd47add65ab8235152d84a32c
Package size
408.1 KiB
Validation
valid · 0 diagnostics
Manifest ID
courses.theoria.polytechnical.data-structures-algorithms
Manifest version
0.2.0
Entry
undefined
Language
en-CA
License
CC-BY-4.0
Authors
APV, Theoria
Subjects
computer science, data structures, algorithms, complexity, graphs, sorting, dynamic programming
Level
foundational
Structure
41 lessons · 71 activities · 210 questions

Learning outcomes

  1. Derive time/space costs from code, recurrences, operation models, case distinctions, and amortized reasoning.
  2. Implement and choose arrays, linked structures, stacks/queues/deques, hash tables, search trees, heaps, and priority queues from workload needs.
  3. Trace and analyse comparison sorting, merge/quicksort, heap sort, lower bounds, and partial selection.
  4. Represent graphs and implement/trace BFS, DFS, connectivity, topological sorting, shortest paths, and minimum spanning trees.
  5. Choose and justify divide-and-conquer, greedy, and dynamic-programming approaches from problem structure.
  6. Prove core algorithms/data-structure operations with invariants, exchange/cut arguments, and explicit preconditions.
  7. Separate asymptotic guarantees from constants/measurements and benchmark deterministic pathological inputs responsibly.
  8. Build and defend a tested dependency/network analysis tool with algorithms, proofs, complexity analysis, and benchmarks.

Release notes

No release notes were provided.

Canonical source is immutable

This release points to the validated source `.mcf.zip`. Browser-compiled learner output is derived and is not treated as repository source.