Syllabus by chapter · অধ্যায়ভিত্তিক সিলেবাস

Arrays & Linked Lists · 55 questions: Array Representation of Binary Tree, O(1) enQueue/deQueue using a singly linked list, BFS vs DFS; Linked List vs Array, Constructing a Binary Tree from Preorder and Postorder Traversal, Types of Linked List, Singly Linked List vs Doubly Linked List
Stacks & Queues · 39 questions: Balanced Parentheses Checking, Stack vs Queue, Infix to Postfix Conversion and Evaluation, Binary Max-Heap Insertion, Tracing Push and Pop Operations on a Stack, Max-Heap Properties & Heap Sort
Trees · 64 questions: Minimum and maximum nodes in a binary tree, Infix to prefix conversion, BST Insertion, Postorder Traversal, and Deletion, Binary Tree Traversal (Inorder/Preorder/Postorder), Proper (Full) Binary Tree - Leaf/Internal Node Relation, Adjacency List vs. Adjacency Matrix for a Complete Binary Tree
Graphs · 11 questions: Adjacency List vs Adjacency Matrix Efficiency, True/False statements on Graphs, Eulerian and Hamiltonian Graphs, Adjacency Matrix vs Adjacency List
Hashing · 8 questions: Linear Probing Collision Resolution, Separate Chaining Collision Resolution, Hash Table — O(1) Average Search, Quadratic Probing Collision Resolution, O(1) average-case search using a hash table
Complexity · 1 questions: Linear vs Non-linear Data Structures; logarithmic-complexity structure
Mixed Topics · 44 questions: Where recursion is used in data structures, Linear বনাম Non-Linear Data Structure, Infix, Prefix, Postfix Notation, Operations on Data Structures; Prefix/Infix/Postfix, Linear vs Non-linear Data Structures, Linear vs Non-Linear Data Structures

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Start learning: Complexity Analysis and Big-O Notation · কমপ্লেক্সিটি অ্যানালাইসিস ও Big-O নোটেশন

Complexity Analysis and Big-O Notation

Introduction

Every program you write consumes two precious resources: time (how long it runs) and space (how much memory it uses). Complexity analysis is the mathematical tool that lets us predict how these resources grow as the input to a program grows — without actually running the program on a specific computer. Big-O notation is the language we use to state the answer.

Why does this matter for exams? In BCS, Bank IT, and IT-officer (Programmer / Assistant Programmer) written and MCQ tests, complexity questions appear almost every year. Typical questions ask: "What is the time complexity of binary search?", "Which is faster for large n: O(n log n) or O(n²)?", or "How many times does this loop run?" If you understand the reasoning behind Big-O — not just memorized values — you can answer any variation they throw at you, including code-tracing questions.

Read the whole lesson free with an account · পুরো পাঠ পড়তে বিনামূল্যে সাইন আপ করুন

All 7 lessons · সব পাঠ

Complexity Analysis and Big-O Notation · কমপ্লেক্সিটি অ্যানালাইসিস ও Big-O নোটেশন
Arrays, Strings and Linked Lists · অ্যারে, স্ট্রিং ও লিংকড লিস্ট
Stacks and Queues · স্ট্যাক এবং কিউ
Trees: BST, AVL and Heaps · ট্রি: BST, AVL এবং Heap
Graphs and Their Representations · গ্রাফ ও তাদের উপস্থাপনা
Hashing and Hash Tables · হ্যাশিং ও হ্যাশ টেবিল
Advanced Data Structure Topics · উন্নত ডেটা স্ট্রাকচার বিষয়

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