---
title: Algorithms & Data Structures
description: The problem-solving core of CS: data structures, algorithm design, complexity, and dynamic programming.
category: programming-tech
url: /subcategory/programming-tech/algorithms-and-data-structures
---

# Algorithms & Data Structures

The problem-solving core of CS: data structures, algorithm design, complexity, and dynamic programming.

## Available Subjects

### Algorithm Complexity

This topic covers the mathematical analysis of computer algorithms, focusing on time and space complexity. Learners will understand how to use Big O notation to evaluate algorithm efficiency, compare performance, and optimize code for large-scale data.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 videos, 1 courses, 1 websites

[Learn Algorithm Complexity](/subject/algorithm-complexity)

### Algorithm Design Techniques

This topic covers systematic methods for solving computational problems, such as recursion, backtracking, and heuristics. Learners will understand how to analyze problem requirements and select or design efficient algorithmic strategies to solve them.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 videos, 1 books, 1 courses, 1 websites

[Learn Algorithm Design Techniques](/subject/algorithm-design-techniques)

### Algorithms

Algorithms are step-by-step procedures used to solve computational problems and process data. Learners will understand sorting, searching, graph traversal, and how to analyze time and space complexity using Big O notation to write efficient code.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 videos, 6 books, 4 courses, 5 websites

[Learn Algorithms](/subject/algorithms)

### Coding Interview Preparation

Technical interviews test data structures, algorithms and system design under time pressure. You will learn the core problem patterns, a practice strategy, behavioural-round preparation and how to study efficiently.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 3 books, 2 courses, 3 websites

[Learn Coding Interview Preparation](/subject/coding-interview-preparation)

### Competitive Programming

Competitive programming is algorithmic problem-solving against the clock. You will learn the standard algorithm toolkit, contest strategy and the training ladders used for Codeforces, ICPC and olympiads.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 books, 1 courses, 1 websites

[Learn Competitive Programming](/subject/competitive-programming)

### Data Structures

Data structures are specialized formats for organizing, processing, and storing data in a computer. Learners will understand arrays, linked lists, stacks, queues, trees, and graphs, and how to select the optimal structure for efficient data retrieval.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 videos, 3 books, 1 courses, 3 websites

[Learn Data Structures](/subject/data-structures)

### Divide and Conquer

This algorithmic paradigm involves breaking a complex problem into smaller subproblems, solving them recursively, and combining their results. Learners will understand how to apply this technique to optimize sorting, searching, and mathematical computations.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 2 websites

[Learn Divide and Conquer](/subject/divide-and-conquer)

### Dynamic Programming

This algorithmic technique solves complex problems by breaking them down into overlapping subproblems and storing intermediate results to avoid redundant calculations. Learners will understand how to identify optimization problems and implement efficient, memoized solutions.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 books, 1 courses, 2 websites

[Learn Dynamic Programming](/subject/dynamic-programming)

### Graph Algorithms

This topic focuses on methods for traversing, searching, and analyzing network structures represented as vertices and edges. Learners will understand how to solve practical problems like finding the shortest path, detecting cycles, and modeling connectivity.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 books, 4 courses, 2 websites

[Learn Graph Algorithms](/subject/graph-algorithms)

### Greedy Algorithms

This algorithmic approach builds up a solution piece by piece, always choosing the next option that offers the most immediate benefit. Learners will understand when this heuristic yields optimal solutions and how to apply it to scheduling and optimization problems.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 books, 1 courses, 1 websites

[Learn Greedy Algorithms](/subject/greedy-algorithms)

### Search Algorithms

This topic covers techniques for retrieving specific information stored within various data structures, ranging from simple linear searches to advanced tree and graph traversals. Learners will understand how to implement and compare the efficiency of different search methodologies.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 books, 3 websites

[Learn Search Algorithms](/subject/search-algorithms)

### Sorting Algorithms

This topic examines the methods used to arrange elements in a specific order, such as numerical or alphabetical. Learners will understand the mechanics, trade-offs, and efficiency profiles of algorithms like quicksort, mergesort, and heapsort.

**Difficulty:** Beginner, Intermediate, Advanced

**Resources:** 1 videos, 1 books, 1 websites

[Learn Sorting Algorithms](/subject/sorting-algorithms)

---

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