CMS 702: Data Structures & Computer Algorithms (3 Units)

Official course outline

Basic algorithmic analysis: Asymptotic analysis of Upper and average complexity bounds; standard Complexity Classes. Time and space tradeoffs in algorithms analysis, recursive algorithms. Algorithmic Strategies: Fundamental computing algorithms: Numerical algorithms, sequential and binary search algorithms; sorting algorithms, Binary Search trees, Hash tables, graphs & its representation. Primitive types, Arrays, Records, Strings and String processing, Data representation in memory, Stack and Heap allocation, Queues, Trees. Implementation Strategies for stack, queues, trees and graphs. Run time Storage management; Pointers and References, linked structures.

Outline vs. what the lecture notes cover

Outline topic	Covered in the lecture notes?
Asymptotic analysis, upper/average bounds	✅ §2
Standard complexity classes	✅ §3
Time and space tradeoffs	⚠️ only touched
Recursive algorithms	⚠️ listed as an algorithm type only
Algorithmic strategies (greedy, divide & conquer, DP, backtracking)	⚠️ named in §1, DP defined in §6
Numerical algorithms	❌ gap
Sequential & binary search	✅ binary search §4; sequential only named
Sorting algorithms	⚠️ types named in §4, no mechanics
Binary search trees	✅ §12–§16 (strongest area)
Hash tables	✅ §5
Graphs & representation	❌ gap
Primitive types, arrays, records	✅ §7 (records light)
Strings & string processing	⚠️ only via the hashing example
Data representation in memory	✅ §7
Stack and heap allocation	⚠️ stack ADT covered §8; heap allocation not
Queues, Trees	✅ §9, §10–§11
Implementation strategies (stack, queue, tree, graph)	⚠️ partial
Run-time storage management	❌ gap
Pointers, references, linked structures	✅ §7

Highest-risk gaps for the exam: graphs & their representation, sorting algorithm mechanics, run-time storage management, numerical algorithms.