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 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.

How to learn DSA?

1. Learn at-least one programming language (C++, Java, Python or JavaScript) and build your basic logic.
2. Learn about Time and Space complexities
3. Learn Data Structures and Algorithms
4. Practice DSA
5. 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

• Logic Building Problems
• Practice Problems on Mathematical Algorithms
• Quiz on 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.

• Complexity Analysis Guide
• Quiz on Complexity Analysis

3. Array

Array is a linear data structure where elements are allocated contiguous memory, allowing for constant-time access.

• Array Data Structure Guide
• Practice Problems on Arrays
• Top 50 Array Coding Problems for Interviews
• Quiz on Arrays

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.

• Guide on Searching Algorithms
• Practice Problems on Searching
• Top 50 Searching Coding Problems for Interviews
• Quiz on Searching

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.

• Guide on Sorting Algorithms
• Practice problems on Sorting algorithm
• Top Sorting Interview Questions and Problems
• Quiz on Sorting

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.

• Guide on Hashing
• Practice Problems on Hashing
• Top 20 Hashing Technique based Interview Questions
• Quiz on Hashing

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.

• Two Pointer Technique
• Practice problems on Two Pointer
• Quiz on Two Pointer Technique
• Top Problems on Two Pointers Technique for Interviews

8. Window Sliding Technique

In Window Sliding Technique, we use the result of previous subarray to quickly compute the result of current.

• Window Sliding Technique
• Practice problems on Window Sliding
• Quiz on Sliding Window
• Top Problems on Sliding Window Technique for Interviews

9. Prefix Sum Technique

In Prefix Sum Technique, we compute prefix sums of an array to quickly find results for a subarray.

• Prefix Sum Technique
• Practice problems on Prefix Sum
  
• Quiz on Prefix Sum
• Top Problems on Prefix Sum Technique for Interviews

10. String

String is a sequence of characters, typically immutable and have limited set of elements (lower case or all English alphabets).

• Guide on Strings
• Practice Problems on String
• Top 50 String Coding Problems for Interviews
• Quiz on Strings

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.

• Guide on Recursive Algorithms
• Practice Problems on Recursion algorithm
• Top 50 Problems on Recursion Algorithm for Interview
• Quiz on Recursion

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.

• Matrix Data Structure Guide
• Practice Problems on Matrix/Grid
• Top 50 Problems on Matrix/Grid for Interviews
• Quiz on Matrix/Grid.

13. 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.

• Stack Data Structure Guide
• Practice Problems on Stack
• Top 50 Problems on Stack for Interviews
• Quiz on Stack

14. 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.

• Queue Data Structure Guide
• Practice Problems on Queue
• Top 50 Problems on Queue for Interviews
• Quiz on Queue

15. Deque

A deque (double-ended queue) is a data structure that allows elements to be added or removed from both ends efficiently. It supports operations like insertion and deletion at both the front and rear, making it more versatile than a standard queue.

• Deque Data Structure Guide
• Top Problems on Deque Data Structure for Interviews
• Quiz on Deque

16. 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.

• Linked List Data Structure Guide
• Practice problems on Linked Lists
• Top 50 Problems on Linked List for Interviews
• Quiz on Linked List

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.

• Tree Data Structure Guide
• Practice Problems on Tree
• Top 50 Tree Coding Problems for Interviews
• Quiz on Tree

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.

• Heap Data Structure Guide
• Practice Problems on Heap
• Top 50 Problems on Heap for Interviews
• Quiz on Heap

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.

• Guide on Graph Algorithms
• Practice Problems on Graph
• Top 50 Problems on Graph for Interviews
• Quiz on Graph

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.

• Guide on Greedy Algorithms
• Practice Problems on Greedy Algorithm
• Top 20 Greedy Algorithm Interview Questions
• Quiz on 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.

• Dynamic Programming Guide
• Practice Problems on Dynamic Programming
• Top 50 Dynamic Programming Coding Problems for Interviews
• Quiz on DP

22. Other Algorithms

Bitwise Algorithms: Operate on individual bits of numbers.

• Bitwise Algorithms Guide
• Practice Problems on Bit Magic
• Quiz on Bit Magic
• Top Problems on Bit Manipulation for Interviews

Backtracking Algorithm : Follow Recursion with the option to revert and traces back if the solution from current point is not feasible.

• Guide on Backtracking Algorithms
• Practice Problems on Backtracking algorithm
• Top 20 Backtracking Algorithm Interview Questions
• Quiz on Backtracking

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.

• Guide on Divide and Conquer Algorithm
• Practice problems on Divide And Conquer algorithm
• Quizzes on Divide and Conquer

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.

• Guide on Branch and Bound Algorithm

Geometric algorithms are a set of algorithms that solve problems related to shapes, points, lines and polygons.

• Guide on Geometric Algorithms
• Practice Problem on Geometric Algorithms

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.

• Guide on Randomized Algorithms

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.

• Guide on Trie Data Structure
• Guide on Segment Tree Data Structure
• Guide on Red-Black Tree
• Guide on Binary Indexed Tree
• Practice Problems on Advanced Data Structure

Cheat Sheets

• Blind 75
• SDE SHEET – A Complete Guide for SDE Preparation
• GeeksforGeeks Master Sheet – List of all Cheat Sheets

Related Article:

• Introduction to DSA

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