Data Structures and Algorithms (DSA) refer to the study of data structures used for organizing and storing data, along with the design of algorithms for solving problems that operate on these data structures.
Why to Learn DSA?
- Whether it’s finding the shortest path in a GPS system or optimizing search results in a search engine, DSA plays an important role to solve such problems.
- By understanding DSA, you can design systems more efficiently, which is very important in areas like web applications, databases, and machine learning etc.
- Mastering DSA is not only important for developing high-quality software but also for career growth. Top Companies like Google, Microsoft, Amazon, Apple, Meta and many other companies heavily focus on data structures and algorithms during interviews.
- Learning DSA boosts your problem-solving abilities and make you a stronger programmer in today’s competitive tech world.
How to learn DSA?
The first and foremost thing is dividing the total procedure into little pieces which need to be done sequentially. The complete process to learn DSA from scratch can be broken into 5 parts:
- Learn at-least one programming language (C++, Java, Python or JavaScript)
- Learn about Time and Space complexities
- Learn Data Structures and Algorithms
- Solve the best-quality problems on GfG Practice.
- Compete in GfG Weekly Coding Contest and 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. 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 of Algorithms
2. Mathematical and Bitwise Algorithms
Mathematical Algorithms
For problem solving, we need to know the basics of Numbers, LCM, GCD, Factorial, Permutation and Combinations.
- Mathematical Algorithms Guide
- Practice Problems on Mathematical Algorithms
Bitwise Algorithms
Bitwise algorithmsare algorithms that operate on individual bits of numbers. These algorithms manipulate the binary representation of numbers like shifting bits, setting or clearing specific bits of a number and perform bitwise operations.
- Bitwise Algorithms Guide
- Practice Problems on Bit Magic
- Quiz on Bit Magic
3. Array
Array is a linear data structure that stores a collection of elements of the same data type. Elements are allocated contiguous memory, allowing for constant-time access. Each element has a unique index number.
- Array Data Structure Guide
- Practice Problems on Arrays
- Top 50 Array Coding Problems for Interviews
4. 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
5. String
String is a sequence of characters, typically used to represent text. It is considered a data type that allows for the manipulation and processing of textual data in computer programs.
- Guide on Strings
- Practice Problems on String
- Top 50 String Coding Problems for Interviews
6. 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, web development, and systems like compilers and browsers.
- Stack Data Structure Guide
- Practice Problems on Stack
- Top 50 Problems on Stack for Interviews
7. 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
8. 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
9. Backtracking Algorithm
Backtracking Algorithm is derived from the Recursion algorithm, with the option to revert if a recursive solution fails, i.e. in case a solution fails, the program traces back to the moment where it failed and builds on another solution. So basically it tries out all the possible solutions and finds the correct one.
- Guide on Backtracking Algorithms
- Practice Problems on Backtracking algorithm
- Top 20 Backtracking Algorithm Interview Questions
10. Divide and Conquer Algorithm
Divide and conquer algorithms follow a recursive 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
11. 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
- Quizzes on Searching
12. 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
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.
- Linked List Data Structure Guide
- Practice problems on Linked Lists
- Top 50 Problems on Linked List for Interviews
14. Hash
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 and plays a key role in software applications like secure data retrieval, password storage, cryptography, digital signatures, etc.
- Guide on Hashing
- Practice Problems on Hashing
- Top 20 Hashing Technique based Interview Questions
15. 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
16. 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
17. 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.
- Graph and its representations
- Practice Problems on Graph
- Top 50 Problems on Graph for Interviews
18. 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
19. 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
20. Graph Algorithms
Graph algorithms are a set of techniques and methods used to solve problems related to graphs, which are a collection of nodes and edges. These algorithms perform various operations on graphs, such as searching, traversing, finding the shortest path, and determining connectivity. They are essential for solving a wide range of real-world problems, including network routing, social network analysis, and resource allocation.
- Guide on Graph Algorithms
- Practice Problems on Graph Algorithms
- Top 50 Graph Coding Problems for Interviews
21. Pattern Searching
Pattern Searching is a fundamental technique in DSA used to find occurrences of a specific pattern within a larger text. The Pattern Searching Algorithms use techniques like preprocessing to minimize unnecessary comparisons, making the search faster.
- Pattern Searching Guide
- Practice Problems on Pattern Searching
22. Branch and Bound Algorithm
Branch and Bound Algorithm is a method 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
23. Geometric Algorithms
Geometric algorithms are a set of algorithms that solve problems related to shapes, points, lines and polygons. Geometric algorithms are essential for solving a wide range of problems in computer science, such as intersection detection, convex hull computation, etc.
- Guide on Geometric Algorithms
- Practice Problem on Geometric Algorithms
24. Randomized 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
Practice Problem Cheat Sheets
- Blind 75
- SDE SHEET – A Complete Guide for SDE Preparation
- GeeksforGeeks Master Sheet – List of all Cheat Sheets
Related Articles:
- Introduction to DSA
- Data Structures Tutorial
- Algorithms Tutorial
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