Exam Prep Guide

Data Structures & Algorithms Exam Prep

A topic-by-topic guide to what's actually covered in Data Structures & Algorithms, how to prepare for it properly, and the mistakes that cost students the most marks.

What's Typically Covered

Introduction & Algorithm Analysis

  • Intro to Data Structures
  • Big O, Big Theta, Big Omega
  • Arrays

Search Algorithms

  • Linear Search
  • Binary Search

Sorting I

  • Insertion Sort
  • Merge Sort

Sorting II

  • Quick Sort

Linked Lists

  • Singly Linked List
  • Doubly Linked List
  • Circular Linked List

Linked Lists Operations

  • Search
  • Sort

Stacks

  • Implementation Using Array
  • Implementation Using Linked List

Stacks Applications

  • Polish Notation
  • Reverse Polish Notation

Queues

  • Singly Queue
  • Deque
  • Circular Queue
  • Priority Queue
  • Queue Using Stacks (and Vice Versa)

Binary Trees

  • Binary Search Tree (BST)
  • AVL Tree
  • B+ Tree

Binary Trees Traversal

  • Tree Traversal
  • Topological Sort

Binary Heaps

  • Min Heap
  • Max Heap
  • Heap Sort

Hash Tables

  • Hash Function
  • Linear Probing
  • Chaining
  • Quadratic Probing

Graphs

  • Graph Representation
  • Graph Traversal (DFS, BFS)

Graph Algorithms

  • Minimum Spanning Tree (Kruskal)
  • Shortest Path (Dijkstra)

Advanced Data Structures

  • Advanced Data Structures

How to Actually Prepare

  1. Trace algorithms by hand on a small example (5–7 elements), step by step — for sorting/traversal questions, examiners grade the trace itself, not just the final answer.
  2. Memorize each structure's Big-O for insert/search/delete as ONE comparison table, not in isolation — most exam questions ask you to pick the right structure for a scenario, which needs the comparison, not isolated facts.
  3. Practice recursive tree/graph problems by drawing the actual call stack, not just the tree — that’s where most execution-order mistakes happen.

Common Mistakes Students Make

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