DSA stands forDataStructures andAlgorithms. Data structures manage how data is stored and accessed. Algorithms focus on processing this data. Examples of data structures are Array, Linked List, Tree and Heap, and examples of algorithms are Binary Search, Quick Sort and Merge Sort.
- Foundation for almost every software like GPS, Search Engines, AI ChatBots, Gaming Apps, Databases, Web Applications, etc
- Top Companies like Google, Microsoft, Amazon, Apple, Metaand many other heavily focus on DSA in interviews.
- Learning DSA boosts your problem-solving abilities and make you a stronger programmer.
Before beginning the DSA journey, it is recommended to learn at-least one programming language (C++,Java,Python,JavaScript or any other language of your choice).
Below are the recommended step by step topics to learn complete DSA.
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
A sequence of characters, typically immutable and have limited set of elements (lower case or all English alphabets).
11. Recursion
A programming technique where a functioncalls 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
A two-dimensional array of elements, arranged inrowsandcolumns. It is represented as a rectangular grid, with each element at the intersection of a row and column.
13. Linked List
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 beaccessed sequentially, starting from the head of list.
14. Stack
A linear data structure that follows theLast 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 theFirst 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 or double-ended queue is a data structure that allows elements to be added or removed from both ends efficiently.
17. Tree
Anon-linear, hierarchicaldata structure consisting of nodes connected by edges, with a top node called therootand nodes having child nodes. It is widely used infile systems,databases,decision-making algorithms, etc.
18. Heap
Acomplete binary tree that satisfies theheap property. Heaps are usually used to implementpriority queues, where thesmallestorlargestelement is always at the root of the tree.
19. Graph
A non-lineardata structure consisting of a finite set ofvertices(or nodes) and a set ofedges(or links)that connect a pair of nodes. Graphs are widely used to represent relationships between entities.
20. Greedy Algorithm
Greedy Algorithmbuilds 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 choosinglocally 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 simplersubproblems. By solving each subproblem only once and storing the results, it avoids redundant computations, leading to more efficient solutionsfor a wide range of problems.
22. Advanced Data Structure and Algorithms
Advanced Data Structures likeTrie,Segment Tree,Red-Black Tree andBinary 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.
23. Other Algorithms
Bitwise Algorithms: Operate on individual bits of numbers.
Backtracking Algorithm : Follow Recursion with the option torevert and traces backif the solution from current point is not feasible.
Divide and conquer: A strategy to solve problems by dividing them intosmaller 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 algorithmsare a set of algorithms that solve problems related to shapes, points, lines and polygons.
Randomized algorithmsare 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.
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