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Summary Data Structures & Algorithms - Exam and Interview Guide

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A complete original study guide for Data Structures and Algorithms: Big-O analysis, arrays, linked lists, stacks, queues, trees, graphs, sorting and dynamic programming. Includes worked code examples, complexity tables, exam tips and practice questions. Ideal for CS exams and coding interviews.

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Data Structures & Algorithms - Exam
and Interview Guide
Big-O, arrays, linked lists, trees, graphs, sorting and dynamic programming -
with worked examples




Complete Study Guide | Key Concepts | Examples | Practice Questions

, 1. Big-O Notation and Complexity Analysis

Big-O describes how runtime or memory grows as input size n grows. It captures the worst case and drops
constants and lower-order terms.

Notation Name Example

O(1) Constant Array index access

O(log n) Logarithmic Binary search

O(n) Linear Single loop

O(n log n) Linearithmic Merge sort

O(n^2) Quadratic Nested loops / bubble sort

O(2^n) Exponential Naive recursion (Fibonacci)


- Always analyse worst-case time AND space complexity.
- A hash map lookup is O(1) on average - the key trick behind many optimal solutions.
- Sorting first (O(n log n)) often unlocks a linear second pass.
- Recursion space = O(depth of call stack); watch for stack overflow.

TIP: When stuck in an interview, first state the brute force and its complexity, then optimise.
Interviewers reward the reasoning.




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