01
DSA Overview
What data structures and algorithms are, and linear vs non-linear structures.
Data Structures and Algorithms Tutorial
Learn the data structures and algorithms every programmer needs, step by step and in Java. Clear diagrams, working code and time and space complexity for arrays, linked lists, stacks, queues, trees, graphs, sorting and searching.
- 73 tutorials
- 12 learning tracks
- Java implementations
- Beginner to advanced
What Are Data Structures and Algorithms?
A data structure is a way of organising and storing data so it can be used efficiently, such as an array, a linked list or a tree. An algorithm is a precise, step-by-step procedure for solving a problem, such as sorting a list or finding the shortest path. Choosing the right data structure and algorithm can make a program thousands of times faster.
Data structureHow data is organised
AlgorithmSteps to solve a problem
Measured byTime and space complexity
Language hereJava
Linear data structures
Elements are arranged in a sequence, each connected to the previous and next: arrays, linked lists, stacks and queues.
What You Will Learn
- How to analyse algorithms with Big-O time and space complexity
- Arrays and the trade-offs between arrays and linked lists
- Singly, doubly and circular linked lists with full Java implementations
- Stacks and queues, including circular queues, priority queues and deques
- Trees: binary trees, BSTs, AVL, red-black, B-trees and traversals
- Graphs: adjacency matrix and list, BFS and DFS
- Sorting and searching algorithms and when to use each one
- How to choose the right data structure for a problem and explain it in interviews
Who this is for
Computer science students, developers preparing for coding interviews, and programmers who want to write faster, more scalable code.
Prerequisites
Basic Java: variables, loops, methods, classes and objects. New to Java? Start with the Core Java tutorial.
DSA Learning Path
Work through the 12 tracks in order: each one builds on the previous. Linear structures come first, then trees and graphs, then sorting and searching.
02
Array
Contiguous storage with constant-time index access.
03
Linked List Basics
Nodes connected by pointers: concepts, types and trade-offs.
04
Singly Linked List
One-directional lists and their core operations in Java.
05
Doubly Linked List
Two-way links for moving forwards and backwards.
06
Circular Linked List
Lists whose last node points back to the first.
07
Stack
Last-in, first-out (LIFO) structure and its implementations.
08
Queue
First-in, first-out (FIFO) structures: circular, priority and double-ended queues.
09
Tree
Hierarchical structures: binary trees, BSTs, balanced trees and B-trees.
10
Graph
Vertices and edges: representations, BFS and DFS.
11
Sorting
Classic sorting algorithms, how they work and their complexity.
12
Searching
Finding elements efficiently in arrays.
Complexity Cheat Sheet
Average-case time complexity of the most common operations. Use it to compare data structures at a glance.
| Data structure | Access | Search | Insert | Delete |
|---|---|---|---|---|
| Array | O(1) | O(n) | O(n) | O(n) |
| Linked list | O(n) | O(n) | O(1)* | O(1)* |
| Stack / Queue | – | O(n) | O(1) | O(1) |
| Binary search tree | – | O(log n) avg, O(n) worst | O(log n) avg | O(log n) avg |
| AVL / Red-black tree | – | O(log n) | O(log n) | O(log n) |
| Hash table | – | O(1) avg | O(1) avg | O(1) avg |
* At the head, or at a known node. Finding the position first takes O(n).
| Algorithm | Best | Average | Worst | Extra space | Stable |
|---|---|---|---|---|---|
| Bubble sort | O(n) | O(n²) | O(n²) | O(1) | Yes |
| Selection sort | O(n²) | O(n²) | O(n²) | O(1) | No |
| Insertion sort | O(n) | O(n²) | O(n²) | O(1) | Yes |
| Merge sort | O(n log n) | O(n log n) | O(n log n) | O(n) | Yes |
| Quick sort | O(n log n) | O(n log n) | O(n²) | O(log n) | No |
| Binary search | O(1) | O(log n) | O(log n) | O(1) | – |
How to Learn DSA Effectively
- Understand the idea. Read the concept and study the diagrams before looking at code.
- Trace it by hand. Walk through operations on paper with a small example.
- Implement it yourself. Write the structure or algorithm in Java without copying.
- Analyse the complexity. Work out the time and space cost of each operation.
- Practise problems. Apply what you learned to coding problems and interview questions.
Why Learn Data Structures and Algorithms?
Write Faster Code
The right structure turns slow, brute-force solutions into efficient ones that scale.
Crack Coding Interviews
DSA is the core of technical interviews at product companies of every size.
Think Like an Engineer
Break complex problems into steps and reason clearly about trade-offs.
Understand Real Systems
Databases, compilers, networks and caches are built on trees, graphs and hash tables.
Use Libraries Wisely
Know when to pick ArrayList, LinkedList, HashMap or TreeMap in Java.
Grow Your Career
Strong fundamentals make it easier to learn new technologies and take on bigger roles.
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