DSA Tutorial - Learn Data Structures and Algorithms
Last Updated : 25 Apr, 2025
DSA (Data Structures and Algorithms) is the study of organizing data efficiently using data structures like arrays, stacks, and trees, paired with step-by-step procedures (or algorithms) to solve problems effectively. Data structures manage how data is stored and accessed, while algorithms focus on processing this data.
Why to Learn DSA?
- Learning DSA boosts your problem-solving abilities and make you a stronger programmer.
- DSA is foundation for almost every software like GPS, Search Engines, AI ChatBots, Gaming Apps, Databases, Web Applications, etc
- Top Companies like Google, Microsoft, Amazon, Apple, Meta and many other heavily focus on DSA in interviews.
Do you know the basics already and looking to prepare in limited time?
Try our free course GfG 160 where we have 160 most asked problems along with well written editorials and video explanations. The course also has 90 bonus problems.
How to learn DSA?
- Learn at-least one programming language (C++, Java, Python or JavaScript) and build your basic logic.
- Learn about Time and Space complexities
- Learn Data Structures (Arrays, Linked List, etc) and Algorithms (Searching, Sorting, etc).
- Once you learn main topics, it is important to solve coding problems against some predefined test cases,
- Solve problems daily using GfG POTD, weekly using GfG Weekly Contest and monthly using GfG Job-A-Thon.
Hoping you have learned a programming language of your choice, here comes the next stage of the roadmap - Learn about Time and Space Complexities.
1. Logic Building
Once you have learned basics of a programming language, it is recommended that you learn basic logic building
2. Learn about Complexities
To analyze algorithms, we mainly measure order of growth of time or space taken in terms of input size. We do this in the worst case scenario in most of the cases. Please refer the below links for a clear understanding of these concepts.
3. Array
Array is a linear data structure where elements are allocated contiguous memory, allowing for constant-time access.
4. Searching Algorithms
Searching algorithms are used to locate specific data within a large set of data. It helps find a target value within the data. There are various types of searching algorithms, each with its own approach and efficiency.
5. Sorting Algorithm
Sorting algorithms are used to arrange the elements of a list in a specific order, such as numerical or alphabetical. It organizes the items in a systematic way, making it easier to search for and access specific elements.
6. Hashing
Hashing is a technique that generates a fixed-size output (hash value) from an input of variable size using mathematical formulas called hash functions. Hashing is commonly used in data structures for efficient searching, insertion and deletion.
7. Two Pointer Technique
In Two Pointer Technique, we typically use two index variables from two corners of an array. We use the two pointer technique for searching a required point or value in an array.
8. Window Sliding Technique
In Window Sliding Technique, we use the result of previous subarray to quickly compute the result of current.
9. Prefix Sum Technique
In Prefix Sum Technique, we compute prefix sums of an array to quickly find results for a subarray.
10. String
String is a sequence of characters, typically immutable and have limited set of elements (lower case or all English alphabets).
11. Recursion
Recursion is a programming technique where a function calls itself within its own definition. It is usually used to solve problems that can be broken down into smaller instances of the same problem.
12. Matrix/Grid
Matrix is a two-dimensional array of elements, arranged in rows and columns. It is represented as a rectangular grid, with each element at the intersection of a row and column.
13. Linked List
Linked list is a linear data structure that stores data in nodes, which are connected by pointers. Unlike arrays, nodes of linked lists are not stored in contiguous memory locations and can only be accessed sequentially, starting from the head of list.
14. Stack
Stack is a linear data structure that follows the Last In, First Out (LIFO) principle. Stacks play an important role in managing function calls, memory, and are widely used in algorithms like stock span problem, next greater element and largest area in a histogram.
15. Queue
Queue is a linear data structure that follows the First In, First Out (FIFO) principle. Queues play an important role in managing tasks or data in order, scheduling and message handling systems.
16. Deque
A deque (double-ended queue) is a data structure that allows elements to be added or removed from both ends efficiently.
17. Tree
Tree is a non-linear, hierarchical data structure consisting of nodes connected by edges, with a top node called the root and nodes having child nodes. It is widely used in file systems, databases, decision-making algorithms, etc.
18. Heap
Heap is a complete binary tree data structure that satisfies the heap property. Heaps are usually used to implement priority queues, where the smallest or largest element is always at the root of the tree.
19. Graph
Graph is a non-linear data structure consisting of a finite set of vertices(or nodes) and a set of edges(or links)that connect a pair of nodes. Graphs are widely used to represent relationships between entities.
20. Greedy Algorithm
Greedy Algorithm builds up the solution one piece at a time and chooses the next piece which gives the most obvious and immediate benefit i.e., which is the most optimal choice at that moment. So the problems where choosing locally optimal also leads to the global solutions are best fit for Greedy.
21. Dynamic Programming
Dynamic Programming is a method used to solve complex problems by breaking them down into simpler subproblems. By solving each subproblem only once and storing the results, it avoids redundant computations, leading to more efficient solutions for a wide range of problems.
22. Other Algorithms
Bitwise Algorithms: Operate on individual bits of numbers.
Backtracking Algorithm : Follow Recursion with the option to revert and traces back if the solution from current point is not feasible.
Divide and conquer: A strategy to solve problems by dividing them into smaller subproblems, solving those subproblems, and combining the solutions to obtain the final solution.
Branch and Bound : Used in combinatorial optimization problems to systematically search for the best solution. It works by dividing the problem into smaller subproblems, or branches, and then eliminating certain branches based on bounds on the optimal solution. This process continues until the best solution is found or all branches have been explored.
Geometric algorithms are a set of algorithms that solve problems related to shapes, points, lines and polygons.
Randomized algorithms are algorithms that use randomness to solve problems. They make use of random input to achieve their goals, often leading to simpler and more efficient solutions. These algorithms may not product same result but are particularly useful in situations when a probabilistic approach is acceptable.
23. Advanced Data Structure and Algorithms
Advanced Data Structures like Trie, Segment Tree, Red-Black Tree and Binary Indexed Tree offer significant performance improvements for specific problem domains. They provide efficient solutions for tasks like fast prefix searches, range queries, dynamic updates, and maintaining balanced data structures, which are crucial for handling large datasets and real-time processing. By optimizing time complexity, they enable better scalability and faster execution in many computational problems.
Cheat Sheets
Similar Reads
DSA Tutorial - Learn Data Structures and Algorithms
DSA (Data Structures and Algorithms) is the study of organizing data efficiently using data structures like arrays, stacks, and trees, paired with step-by-step procedures (or algorithms) to solve problems effectively. Data structures manage how data is stored and accessed, while algorithms focus on
7 min read
Array Data Structure Guide
In this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
4 min read
String in Data Structure
A string is a sequence of characters. The following facts make string an interesting data structure. Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immu
3 min read
Matrix Data Structure
Matrix Data Structure is a two-dimensional array arranged in rows and columns. It is commonly used to represent mathematical matrices and is fundamental in various fields like mathematics, computer graphics, and data processing. Matrices allow for efficient storage and manipulation of data in a stru
2 min read
Searching Algorithms
Searching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
3 min read
Sorting Algorithms
A Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read
Hashing in Data Structure
Hashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
3 min read
Two Pointers Technique
Two pointers is really an easy and effective technique that is typically used for Two Sum in Sorted Arrays, Closest Two Sum, Three Sum, Four Sum, Trapping Rain Water and many other popular interview questions. Given a sorted array arr (sorted in ascending order) and a target, find if there exists an
12 min read
Sliding Window Technique
Sliding Window problems involve moving a fixed or variable-size window through a data structure, typically an array or string, to solve problems efficiently based on continuous subsets of elements. This technique is used when we need to find subarrays or substrings according to a given set of condit
15+ min read
Prefix Sum Array - Implementation and Applications
Given an array arr[] of size n, the task is to find the prefix sum of the array. A prefix sum array is another array prefixSum[] of the same size, such that prefixSum[i] is arr[0] + arr[1] + arr[2] . . . arr[i]. Examples: Input: arr[] = [10, 20, 10, 5, 15]Output: 10 30 40 45 60Explanation: For each
8 min read