A doubly linked list has two pointers โleftโ and โrightโ which enable it to traverse in either direction. Compared to singly liked list which has only a โnextโ pointer, doubly linked list requires extra space to store this extra pointer. Every insertion and deletion requires manipulation of two pointers, hence it takes a bit longer time. Implementing doubly linked list involves setting both left and right pointers to correct nodes and takes more time than singly linked list.
๐7
https://topmate.io/coding/886874
If you're a job seeker, these well structured document DSA resources will help you to know and learn all the real time DSA & OOPS Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide!
Please use the above link to avail them!๐
NOTE: -Most people hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your job search journey... All the best!๐โ๏ธ
If you're a job seeker, these well structured document DSA resources will help you to know and learn all the real time DSA & OOPS Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide!
Please use the above link to avail them!๐
NOTE: -Most people hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your job search journey... All the best!๐โ๏ธ
๐8โค2
Programming is no longer about how well you google search.
Programming is now about how well you can write prompts for an ai system to generate code for you
Programming is now about how well you can write prompts for an ai system to generate code for you
๐21๐6โค3๐1
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://t.me/free4unow_backup
All the best ๐๐
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://t.me/free4unow_backup
All the best ๐๐
๐10
How to guess the solution for DSA problems?
Yes, it is possible.
You can predict the solution for a problem by analyzing the constraints.
Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.
Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.
Here's a quick guide based on the value of (n):
- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).
Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
Yes, it is possible.
You can predict the solution for a problem by analyzing the constraints.
Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.
Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.
Here's a quick guide based on the value of (n):
- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).
Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐9
Top 9 Http Methods-
GET ๐ง - Retrieve data from a resource.
HEAD ๐ง - Retrieve the headers of a resource.
POST ๐ฎ - Submit data to a resource.
PUT ๐ฅ - Update an existing resource or create a new resource.
DELETE ๐๏ธ - Remove a resource.
CONNECT ๐ - Establish a network connection for a resource.
OPTIONS โ๏ธ - Describe communication options for the target resource.
TRACE ๐ต๏ธโโ๏ธ - Retrieve a diagnostic trace of the request.
PATCH ๐ฉน - Apply a partial update to a resource.
GET ๐ง - Retrieve data from a resource.
HEAD ๐ง - Retrieve the headers of a resource.
POST ๐ฎ - Submit data to a resource.
PUT ๐ฅ - Update an existing resource or create a new resource.
DELETE ๐๏ธ - Remove a resource.
CONNECT ๐ - Establish a network connection for a resource.
OPTIONS โ๏ธ - Describe communication options for the target resource.
TRACE ๐ต๏ธโโ๏ธ - Retrieve a diagnostic trace of the request.
PATCH ๐ฉน - Apply a partial update to a resource.
โค6๐5
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Problem: Given an array a of n integers, find all such elements a[i], a[j], a[k], and a[l], such that a[i] + a[j] + a[k] + a[l] = target? Output all unique quadruples.
Solution: of course one way would be to just use 4 nested loops to iterate over all possible quadruples, but this is quite slow O(n^4). Another way is to iterate over all triples, put the sums into a set and then in another pass over elements a[i] check if we have any triple with sum (T - a[i]). This would give us O(n^3), and we need to keep track of which elements gave us the required sums. Another step is to iterate over all pairs and put results into a map from integer to indexes of elements, which produce this sum. Then in another pass over this map we can see if we can get a sum of T using two different values from the map (and they shouldn't be using the same element twice). This approach has time complexity O(n^2).
Solution: of course one way would be to just use 4 nested loops to iterate over all possible quadruples, but this is quite slow O(n^4). Another way is to iterate over all triples, put the sums into a set and then in another pass over elements a[i] check if we have any triple with sum (T - a[i]). This would give us O(n^3), and we need to keep track of which elements gave us the required sums. Another step is to iterate over all pairs and put results into a map from integer to indexes of elements, which produce this sum. Then in another pass over this map we can see if we can get a sum of T using two different values from the map (and they shouldn't be using the same element twice). This approach has time complexity O(n^2).
๐6โค3
Solve these 16 problem solving patterns to learn enough DSA
1. Sliding window pattern.
2. Two pointer pattern.
3. Fast & slow pointers pattern.
4. Merger interval pattern.
5. Cyclic sort pattern.
6. In-place reversal of linked list pattern.
7. Tree breadth first search pattern.
8. Depth first search DFS pattern.
9. Two heap pattern.
10. Subsets pattern.
11. Modified binary search pattern.
12. Bitwise xor pattern.
13. Top 'K' elements pattern.
14. K-way merge pattern.
15. 0/1 knapsack DP pattern.
16. Topological sort Graph pattern.
1. Sliding window pattern.
2. Two pointer pattern.
3. Fast & slow pointers pattern.
4. Merger interval pattern.
5. Cyclic sort pattern.
6. In-place reversal of linked list pattern.
7. Tree breadth first search pattern.
8. Depth first search DFS pattern.
9. Two heap pattern.
10. Subsets pattern.
11. Modified binary search pattern.
12. Bitwise xor pattern.
13. Top 'K' elements pattern.
14. K-way merge pattern.
15. 0/1 knapsack DP pattern.
16. Topological sort Graph pattern.
โค19๐9
๐ฆ๐๐ฒ๐ฝ๐ ๐ง๐ผ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐๐ผ๐ฟ ๐ฎ ๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐11โค2๐1
Beginnerโs Roadmap to Learn Data Structures & Algorithms
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐7
Is DSA important for interviews?
Yes, DSA (Data Structures and Algorithms) is very important for interviews, especially for software engineering roles.
I often get asked, What do I need to start learning DSA?
Here's the roadmap for getting started with Data Structures and Algorithms (DSA):
๐ฃ๐ต๐ฎ๐๐ฒ ๐ญ: ๐๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐
1. Introduction to DSA
- Understand what DSA is and why it's important.
- Overview of complexity analysis (Big O notation).
2. Complexity Analysis
- Time Complexity
- Space Complexity
3. Basic Data Structures
- Arrays
- Linked Lists
- Stacks
- Queues
4. Basic Algorithms
- Sorting (Bubble Sort, Selection Sort, Insertion Sort)
- Searching (Linear Search, Binary Search)
5. OOP (Object-Oriented Programming)
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฎ: ๐๐ป๐๐ฒ๐ฟ๐บ๐ฒ๐ฑ๐ถ๐ฎ๐๐ฒ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐
1. Two Pointers Technique
- Introduction and basic usage
- Problems: Pair Sum, Triplets, Sorted Array Intersection etc..
2. Sliding Window Technique
- Introduction and basic usage
- Problems: Maximum Sum Subarray, Longest Substring with K Distinct Characters, Minimum Window Substring etc..
3. Line Sweep Algorithms
- Introduction and basic usage
- Problems: Meeting Rooms II, Skyline Problem
4. Recursion
5. Backtracking
6. Sorting Algorithms
- Merge Sort
- Quick Sort
7. Data Structures
- Hash Tables
- Trees (Binary Trees, Binary Search Trees)
- Heaps
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฏ: ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐
1. Graph Algorithms
- Graph Representation (Adjacency List, Adjacency Matrix)
- BFS (Breadth-First Search)
- DFS (Depth-First Search)
- Shortest Path Algorithms (Dijkstra's, Bellman-Ford)
- Minimum Spanning Tree (Kruskal's, Prim's)
2. Dynamic Programming
- Basic Problems (Fibonacci, Knapsack etc..)
- Advanced Problems (Longest Increasing Subsea mice, Matrix Chain Subsequence, Multiplication etc..)
3. Advanced Trees
- AVL Trees
- Red-Black Trees
- Segment Trees
- Trie
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฐ: ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป
1. Competitive Programming Platforms: LeetCode, Codeforces, HackerRank, CodeChef Solve problems daily
2. Mock Interviews
- Participate in mock interviews to simulate real interview scenarios.
- DSA interviews assess your ability to break down complex problems into smaller steps.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
Yes, DSA (Data Structures and Algorithms) is very important for interviews, especially for software engineering roles.
I often get asked, What do I need to start learning DSA?
Here's the roadmap for getting started with Data Structures and Algorithms (DSA):
๐ฃ๐ต๐ฎ๐๐ฒ ๐ญ: ๐๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐
1. Introduction to DSA
- Understand what DSA is and why it's important.
- Overview of complexity analysis (Big O notation).
2. Complexity Analysis
- Time Complexity
- Space Complexity
3. Basic Data Structures
- Arrays
- Linked Lists
- Stacks
- Queues
4. Basic Algorithms
- Sorting (Bubble Sort, Selection Sort, Insertion Sort)
- Searching (Linear Search, Binary Search)
5. OOP (Object-Oriented Programming)
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฎ: ๐๐ป๐๐ฒ๐ฟ๐บ๐ฒ๐ฑ๐ถ๐ฎ๐๐ฒ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐
1. Two Pointers Technique
- Introduction and basic usage
- Problems: Pair Sum, Triplets, Sorted Array Intersection etc..
2. Sliding Window Technique
- Introduction and basic usage
- Problems: Maximum Sum Subarray, Longest Substring with K Distinct Characters, Minimum Window Substring etc..
3. Line Sweep Algorithms
- Introduction and basic usage
- Problems: Meeting Rooms II, Skyline Problem
4. Recursion
5. Backtracking
6. Sorting Algorithms
- Merge Sort
- Quick Sort
7. Data Structures
- Hash Tables
- Trees (Binary Trees, Binary Search Trees)
- Heaps
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฏ: ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐
1. Graph Algorithms
- Graph Representation (Adjacency List, Adjacency Matrix)
- BFS (Breadth-First Search)
- DFS (Depth-First Search)
- Shortest Path Algorithms (Dijkstra's, Bellman-Ford)
- Minimum Spanning Tree (Kruskal's, Prim's)
2. Dynamic Programming
- Basic Problems (Fibonacci, Knapsack etc..)
- Advanced Problems (Longest Increasing Subsea mice, Matrix Chain Subsequence, Multiplication etc..)
3. Advanced Trees
- AVL Trees
- Red-Black Trees
- Segment Trees
- Trie
๐ฃ๐ต๐ฎ๐๐ฒ ๐ฐ: ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป
1. Competitive Programming Platforms: LeetCode, Codeforces, HackerRank, CodeChef Solve problems daily
2. Mock Interviews
- Participate in mock interviews to simulate real interview scenarios.
- DSA interviews assess your ability to break down complex problems into smaller steps.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐18โค4
When youโre in an interview, itโs super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
โค ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ข๐๐ฒ๐ฟ๐๐ถ๐ฒ๐:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
โค ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ฆ๐๐ฎ๐๐ฒ๐บ๐ฒ๐ป๐:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
โค ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ฑ ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
โค ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ผ๐น๐ฒ:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure itโs clear whether you were leading the project, a key player, or supporting the team.
โค ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ผ๐ผ๐น๐:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
โค ๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐๐ฐ๐ต๐ถ๐ฒ๐๐ฒ๐บ๐ฒ๐ป๐๐:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got.
โค ๐ง๐ฒ๐ฎ๐บ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the teamโs success?
โค ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐:
- Reflect on what you learned from the project. What new skills did you gain, and what would you do differently next time?
โค ๐ง๐ถ๐ฝ๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- If thereโs a pause after you describe the project, donโt hesitate to ask if theyโd like more details or if thereโs a specific part theyโre interested in.
By preparing your project details thoroughly and understanding what the interviewer is looking for, you can talk about your experience in a way that really showcases your skills and increases your chances of getting the job.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
โค ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ข๐๐ฒ๐ฟ๐๐ถ๐ฒ๐:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
โค ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ฆ๐๐ฎ๐๐ฒ๐บ๐ฒ๐ป๐:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
โค ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ฑ ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
โค ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ผ๐น๐ฒ:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure itโs clear whether you were leading the project, a key player, or supporting the team.
โค ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ผ๐ผ๐น๐:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
โค ๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐๐ฐ๐ต๐ถ๐ฒ๐๐ฒ๐บ๐ฒ๐ป๐๐:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got.
โค ๐ง๐ฒ๐ฎ๐บ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the teamโs success?
โค ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐:
- Reflect on what you learned from the project. What new skills did you gain, and what would you do differently next time?
โค ๐ง๐ถ๐ฝ๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- If thereโs a pause after you describe the project, donโt hesitate to ask if theyโd like more details or if thereโs a specific part theyโre interested in.
By preparing your project details thoroughly and understanding what the interviewer is looking for, you can talk about your experience in a way that really showcases your skills and increases your chances of getting the job.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐11
๐ Here is a complete roadmap to learn Data Structures and Algorithms (DSA) ๐
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐8
๐ฆ๐๐ฒ๐ฝ๐ ๐ง๐ผ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐๐ผ๐ฟ ๐ฎ ๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐4
โญ WIPRO INTERVIEW EXPERIENCE โญ
1) Intro.
2) Willing to Relocate.
3) Service Agreement.
4) about Internship (if you have done)
(details like what did you do there or
learnt there, how many other people
you worked with).
5) About Project ( details like name,
what made you do that project, what
Tech Stacks used, Difficulties faced,
whats the use of that project ).
6) In which Programming language you
are proficient (i said python ).
7) Why python and why not any other
languages.
8) Any one simple Theoritical question
from that language you mentioned
(asked limitations of python).
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
1) Intro.
2) Willing to Relocate.
3) Service Agreement.
4) about Internship (if you have done)
(details like what did you do there or
learnt there, how many other people
you worked with).
5) About Project ( details like name,
what made you do that project, what
Tech Stacks used, Difficulties faced,
whats the use of that project ).
6) In which Programming language you
are proficient (i said python ).
7) Why python and why not any other
languages.
8) Any one simple Theoritical question
from that language you mentioned
(asked limitations of python).
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐15โค1
Best suited IDE's for programming languages:
1. JavaScript => VSCode
2. Python => PyCharm
3. C# => Visual Studio
4. Java => IntelliJ IDEA
5. Ruby => Ruby Mine
6. C & C++ => CLion
1. JavaScript => VSCode
2. Python => PyCharm
3. C# => Visual Studio
4. Java => IntelliJ IDEA
5. Ruby => Ruby Mine
6. C & C++ => CLion
๐18
PREPARING FOR AN ONLINE INTERVIEW?
10 basic tips to consider when invited/preparing for an online interview:
1. Get to know the online technology that the interviewer(s) will use. Is it a phone call, WhatsApp, Skype or Zoom interview? If not clear, ask.
2. Familiarize yourself with the online tools that youโll be using. Understand how Zoom/Skype works and test it well in advance. Test the sound and video quality.
3. Ensure that your internet connection is stable. If using mobile data, make sure itโs adequate to sustain the call to the end.
4. Ensure the lighting and the background is good. Remove background clutter. Isolate yourself in a place where youโll not have any noise distractions.
5. For Zoom/Skype calls, use your desktop or laptop instead of your phone. Theyโre more stable especially for video calls.
6. Mute all notifications on your computer/phone to avoid unnecessary distractions.
7. Ensure that your posture is right. Just because itโs a remote interview does not mean you slouch on your couch. Maintain an upright posture.
8. Prepare on the other job specifics just like you would for a face-to-face interview
9. Dress up like you would for a face-to-face interview.
10. Be all set at least 10 minutes to the start of interview.
10 basic tips to consider when invited/preparing for an online interview:
1. Get to know the online technology that the interviewer(s) will use. Is it a phone call, WhatsApp, Skype or Zoom interview? If not clear, ask.
2. Familiarize yourself with the online tools that youโll be using. Understand how Zoom/Skype works and test it well in advance. Test the sound and video quality.
3. Ensure that your internet connection is stable. If using mobile data, make sure itโs adequate to sustain the call to the end.
4. Ensure the lighting and the background is good. Remove background clutter. Isolate yourself in a place where youโll not have any noise distractions.
5. For Zoom/Skype calls, use your desktop or laptop instead of your phone. Theyโre more stable especially for video calls.
6. Mute all notifications on your computer/phone to avoid unnecessary distractions.
7. Ensure that your posture is right. Just because itโs a remote interview does not mean you slouch on your couch. Maintain an upright posture.
8. Prepare on the other job specifics just like you would for a face-to-face interview
9. Dress up like you would for a face-to-face interview.
10. Be all set at least 10 minutes to the start of interview.
๐6
๐ ๐๐ถ๐ฝ๐ ๐ณ๐ผ๐ฟ ๐ป๐ฒ๐ ๐ฐ๐ผ๐ฑ๐ฒ๐ฟ๐: ๐
1. Learn Fundamentals: Use W3Schools, FreeCodeCamp, or MDN for solid basics.
2. Watch and Code Along: Follow YouTube tutorials to code in real-time.
3. Practice Regularly: Build small projects to sharpen your skills.
4. Join Coding Communities: Engage on platforms like X, Discord, and Reddit for support.
5. Use AI Tools Wisely: Leverage tools like ChatGPT responsibly to aid learning.
6. Master Git and Version Control: Learn to manage your code effectively.
7. Stay Updated: Follow tech blogs, newsletters, and podcasts.
8. Network: Attend meetups, hackathons, and online coding events.
9. Explore Open Source: Contribute to projects to gain experience.
10.Never Stop Learning: Technology evolvesโkeep exploring new languages and frameworks.
Best Programming Resources: https://topmate.io/coding/886839
All the best ๐๐
1. Learn Fundamentals: Use W3Schools, FreeCodeCamp, or MDN for solid basics.
2. Watch and Code Along: Follow YouTube tutorials to code in real-time.
3. Practice Regularly: Build small projects to sharpen your skills.
4. Join Coding Communities: Engage on platforms like X, Discord, and Reddit for support.
5. Use AI Tools Wisely: Leverage tools like ChatGPT responsibly to aid learning.
6. Master Git and Version Control: Learn to manage your code effectively.
7. Stay Updated: Follow tech blogs, newsletters, and podcasts.
8. Network: Attend meetups, hackathons, and online coding events.
9. Explore Open Source: Contribute to projects to gain experience.
10.Never Stop Learning: Technology evolvesโkeep exploring new languages and frameworks.
Best Programming Resources: https://topmate.io/coding/886839
All the best ๐๐
๐9โค2