Level 2 · M.Sc

Algorithms & Data Structures

Syllabus · 42 phases · ~1 year

Every unit this programme teaches, in the order it is taught. Headlines only — the material itself opens once you are enrolled.

Dates, price and prerequisites

Phase 0 Foundations & tooling
Phase 1 Models of computation & counting steps
Phase 2 Asymptotic notation
Phase 3 Recursion & recurrences
Phase 4 Correctness: invariants & proofs
Phase 5 Amortized analysis
Phase 6 Randomness in algorithms
Phase 7 Arrays & linked lists
Phase 8 Stacks, queues & deques
Phase 9 Binary trees & BSTs
Phase 10 Heaps & priority queues
Phase 11 Hash tables: chaining, open addressing & Robin-Hood
Phase 12 Balanced search trees I: AVL
Phase 13 Balanced search trees II: red–black
Phase 14 Disjoint sets (union–find)
Phase 15 Elementary sorting & binary search
Phase 16 Mergesort, quicksort & maximum subarray
Phase 17 Heapsort, the lower bound & linear-time sorts
Phase 18 Selection & order statistics
Phase 19 Greedy algorithms
Phase 20 Dynamic programming I
Phase 21 Dynamic programming II
Phase 22 Backtracking & branch-and-bound
Phase 23 Number-theoretic algorithms
Phase 24 Polynomials & the Fast Fourier Transform
Phase 25 Matrix & algebraic algorithms
Phase 26 Graph representations & BFS
Phase 27 DFS, topological sort, SCC & 2-SAT
Phase 28 Shortest paths I: Dijkstra
Phase 29 Shortest paths II: Bellman–Ford & Floyd–Warshall
Phase 30 Minimum spanning trees
Phase 31 Maximum flow & bipartite matching
Phase 32 String matching: Rabin–Karp, KMP & Z
Phase 33 Tries & prefix structures
Phase 34 Suffix arrays: an introduction
Phase 35 The competitive solver
Phase 36 Benchmarking & profiling in practice
Phase 37 Testing algorithms
Phase 38 Intractability primer
Phase 39 Capstone: algoforge is complete
Phase 40 Hardening, postmortem & "What's Next"
Phase 41 Portal integration