10 DSA cheatsheets for your interview preparation

10 DSA cheatsheets for your interview preparation

DSA cheatsheets are a dime a dozen on the internet. But which ones are actually worth your time? In this article post, we’ll go over 10 of the best DSA cheatsheets out there, covering topics like data structures, algorithms, and dynamic programming. So whether you’re just getting started with DSA or you’re looking for a quick refresher, these cheatsheets will have you covered.

DSA Cheatsheet #1: Data Structures

There are four main data structures in computer science: arrays, linked lists, trees, and hash tables.
Arrays: An array is a collection of data items that are accessed by an index. Arrays are often used to represent vectors or matrices.

Linked Lists: A linked list is a collection of data items that are connected together by links. Linked lists are often used to represent stacks or queues.

Trees: A tree is a collection of data items that are connected together by branches. Trees are often used to represent hierarchical data, such as file systems or family trees.

Hash Tables: A hash table is a collection of data items that are accessed by keys. Hash tables are often used to represent dictionaries or sets.

DSA Cheatsheet #2: Algorithms

The 2nd DSA cheatsheet in the list is Algorithms. An algorithm is a set of instructions or rules that are followed in order to solve a problem. There are many different types of algorithms, and each one is designed to solve a specific type of problem. Algorithms can be used for everything from sorting a list of numbers to finding the shortest path between two points on a map. In computer science, an algorithm is considered to be efficient if it can be executed in a reasonable amount of time and space.
There are dozens of different algorithms that you could potentially be asked about in an interview.

Sorting: Sorting algorithms are used to sort data so that it can be easily accessed or processed. Common sorting algorithms include quicksort, heapsort, and mergesort.

Searching: Searching algorithms are used to find specific items in a data set. Common search algorithms include linear search and binary search.

Graph traversal: Graph traversal algorithms are used to visit all nodes in a graph. Common graph traversal algorithms include depth-first search and breadth-first search.

Pathfinding: Pathfinding algorithms are used to find the shortest path between two points in a graph. Common pathfinding algorithms include Dijkstra’s algorithm.

DSA Cheatsheet #3: System Design

In this section, we will be discussing system design. This is another important DSA cheatsheets, as it tests your ability to think through and design complex systems. We will cover the basics of system design, including how to approach a system design problem, common architectures, and tradeoffs. When approaching a system design problem, it is important to first understand the requirements. What are the goals of the system? What are the constraints? Once you have a good understanding of the problem, you can start brainstorming possible solutions. It is often helpful to draw out your ideas on paper or a whiteboard. There are many different architectures that can be used for designing a system. The most common ones are client-server, peer-to-peer, and distributed systems. Each has its own benefits and tradeoffs that need to be considered when choosing one for your design.

Systems need to be designed with scalability in mind. As the system grows, more users will be added and more data will be processed. The system needs to be able to handle this increased load without performance degradation. There are many factors to consider when designing for scalabilities, such as caching, sharding, and replication.

Performance is another key consideration in system design. The goal is to make the system as fast as possible while still maintaining correctness. There are many optimization techniques that can be used to improve performance, such as caching and pre-computation.

DSA Cheatsheet #4: Behavioral Questions

It’s no secret that Behavioral Questions are becoming increasingly popular with hiring managers. After all, they’re a great way to gauge a candidate’s potential fit for a role. But what exactly is a Behavioral Question? A Behavioral Question is simply a question that asks the candidate to describe how they have behaved in specific situations in the past. For example, a common behavioral question is “Tell me about a time when you had to deal with a difficult customer.”

Behavioral questions are often used to assess three key areas:

Problem Solving: Can the candidate identify and solve problems quickly and effectively?

Communication: Can the candidate communicate clearly and concisely, both verbally and written?

Leadership: Does the candidate demonstrate qualities such as initiative, decisiveness, and teamwork?

DSA Cheatsheet #5: Technical Questions

1. What is the difference between a process and a thread?

2. What is a race condition? How can it be prevented?

3. What is the difference between an interrupt and a trap?

4. What is the difference between virtual memory and physical memory?

5. What is the difference between a cache miss and a cache hit?

DSA Cheatsheet #6: Time Complexity

Time complexity is a measure of how long an algorithm takes to run. The time complexity of an algorithm is the number of operations that the algorithm performs. The time complexity of an algorithm is the number of steps that the algorithm takes. The time complexity of an algorithm is the number of instructions that the algorithm executes.

DSA Cheatsheet #7 Sorting Algorithms

There are a variety of sorting algorithms, each with its own strengths and weaknesses. When choosing a sorting algorithm for your project, it is important to consider the following factors:

  • The size of the data set
  • The type of data
  • The sort order
  • Performance requirements

Below is a DSA cheatsheets of some commonly used sorting algorithms, along with their time complexity in both worst and best cases.

Algorithms Worst case Best case
Bubble SortO(n^2)O(n)
Selection SortO(n^2)O(n^2
Insertion SortO(n^2)O(n)
Merge SortO(nlogn)O(nlogn)
Quick SortO(n^2)O(nlogn)
Algorithm Worst Case Best Case

DSA Cheatsheet #8: Search Algorithms

There are a variety of search algorithms out there, and it can be helpful to know the basics of each one. This DSA cheat sheet will outline some of the most popular search algorithms, and explain when you might want to use each one.

1. Linear search: This is the simplest search algorithm, and it just involves looking through a list of items one by one until you find the target item. This can be practical if the list is short, but it becomes very inefficient as the list gets longer.

2. Binary search: This algorithm is used on sorted lists, and it works by dividing the list in half and searching either the left or right side depending on whether the target value is less than or greater than the middle value. This approach is much faster than a linear search but only works if the list is already sorted.

3. Depth-first search: This algorithm explores a tree or graph by going down each branch as far as possible before backtracking and exploring other branches. This can be helpful for finding paths through large graphs, but it might not find the shortest path if the branches are very long.

4. Breadth-first search: The opposite of depth-first search, this algorithm explores a tree or graph by going across each level before moving down to the next level. This guarantees that the shortest path will be found, but it might take longer if there are a lot of levels in the tree or graph.

DSA Cheatsheet #9: Dynamic programming

Dynamic programming is a method for solving complex problems by breaking them down into smaller, simpler subproblems. Unlike other methods such as greedy algorithms, dynamic programming guarantees that an optimal solution will be found in all cases – not just some cases. To solve a problem using dynamic programming, you first need to identify the subproblems within the larger problem. Each subproblem must then be solved independently, with the results being saved so that they can be reused (“memoized”) when needed. Finally, the solutions to the subproblems are combined to give a solution to the original problem.
Dynamic programming can be used to solve problems in many different fields, including mathematics, computer science, and engineering.

DSA Cheatsheet #10: Hash Table

The last DSA cheatsheet on the list is the Hash Table. A hash table is a data structure that is used to store keys and values. The key is used to access the value, which is stored in the table. A hash table can be used to implement a dictionary, or it can be used to store data in a database. A hash table is usually implemented with an array. Each element in the array is called a bucket. The size of the array is called the capacity. The capacity is typically a power of two.
The key is hashed to an index in the array. The value is then stored at that index. To retrieve the value, the key is hashed again and the value is retrieved from the bucket at that index. Hash tables are efficient because they have low collision rates. That means that when two keys are hashed to the same index, they are more likely to have different values than if they were stored in separate buckets.


Data Structure and Algorithms (DSA) are important concepts in computer science that every software engineer should

If you’re preparing for a DSA interview, these DSA cheatsheets will come in handy. They cover all the essential topics you need to know, from data structures and algorithms to system design. Bookmark this page so you can easily refer back to it during your preparation. And if you have any questions, feel free to post them in the comments section below. Good luck!

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