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 👍👍
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 👍👍
👍7
Object-Oriented Design (OOD) Interview
Interview-ready:
Tools and courses to help you prepare for OOD interviews.
Educative:
Interactive learning paths for mastering design patterns and OOD principles.
Codemia io
They have recently added OOAD sections with many questions like parking lot design, vending machine design and much more.
Head First Design Patterns Book:
An engaging book that simplifies complex design patterns with practical examples.
Interview-ready:
Tools and courses to help you prepare for OOD interviews.
Educative:
Interactive learning paths for mastering design patterns and OOD principles.
Codemia io
They have recently added OOAD sections with many questions like parking lot design, vending machine design and much more.
Head First Design Patterns Book:
An engaging book that simplifies complex design patterns with practical examples.
👍7
✅ Learn Trending Skills in 2025 🔰
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26. Cybersecurity ➝
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29. Deep Learning ➝
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30. Compiler Design ➝
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31. DSA ➝
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32. Prompt Engineering ➝
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Join @free4unow_backup for more free courses
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1. Web Development ➝
◀️ https://t.me/webdevcoursefree
2. CSS ➝
◀️ http://css-tricks.com
3. JavaScript ➝
◀️ http://t.me/javascript_courses
4. React ➝
◀️ http://react-tutorial.app
5. Tailwind CSS ➝
◀️ http://scrimba.com
6. Data Science ➝
◀️ https://t.me/datasciencefun
7. Python ➝
◀️ http://pythontutorial.net
8. SQL ➝
◀️ https://t.me/sqlanalyst
◀️ https://stratascratch.com/?via=free
9. Git and GitHub ➝
◀️ http://GitFluence.com
10. Blockchain ➝
◀️ https://t.me/Bitcoin_Crypto_Web
11. Mongo DB ➝
◀️ http://mongodb.com
12. Node JS ➝
◀️ http://nodejsera.com
13. English Speaking ➝
◀️ https://t.me/englishlearnerspro
14. C#➝
◀️https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/
15. Excel➝
◀️ https://t.me/excel_analyst
16. Generative AI➝
◀️ https://t.me/generativeai_gpt
17. App Development ➝
◀️ https://t.me/appsuser
18. Power BI ➝
◀️ https://t.me/powerbi_analyst
19. Tableau ➝
◀️ https://www.tableau.com/learn/training
20. Machine Learning ➝
◀️ http://developers.google.com/machine-learning/crash-course
21. Artificial intelligence ➝
◀️ http://t.me/machinelearning_deeplearning/
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◀️ https://medium.com/@data_analyst
◀️ https://www.linkedin.com/company/sql-analysts
23. Java ➝
◀️ https://t.me/Java_Programming_Notes
◀️ http://learn.microsoft.com/shows/java-for-beginners/
24. C/C++ ➝
◀️ http://imp.i115008.net/kjoq9V
◀️ https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
25. Data Structures ➝
◀️ https://leetcode.com/study-plan/data-structure/
26. Cybersecurity ➝
◀️ https://t.me/EthicalHackingToday
27. Linux ➝
◀️ https://bit.ly/3KhPdf1
◀️ https://training.linuxfoundation.org/resources/
28. Typescript ➝
◀️ http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
29. Deep Learning ➝
◀️ http://introtodeeplearning.com
30. Compiler Design ➝
◀️ http://online.stanford.edu/courses/soe-ycscs1-compilers
31. DSA ➝
◀️ http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
32. Prompt Engineering ➝
◀️ https://www.promptingguide.ai/
◀️ https://t.me/aiindi
Join @free4unow_backup for more free courses
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👍7❤2👏1
Mastering LinkedLists: Key Questions You Should Know
Easy:
📌 Reverse Linked List: https://lnkd.in/g7qP9-YU
📌 Merge Two Sorted Lists: https://lnkd.in/gRfC6yyF
📌 Remove Nth Node From End of List: https://lnkd.in/gGnGF75X
📌 Delete Node in a Linked List: https://lnkd.in/gqzDgFpN
📌 Palindrome Linked List: https://lnkd.in/gmEjY4gr
Medium:
📌 Add Two Numbers: https://lnkd.in/gvDxHySa
📌 Swap Nodes in Pairs: https://lnkd.in/gnhqwidB
📌 Odd Even Linked List: https://lnkd.in/gS2QpJAw
📌 Intersection of Two Linked Lists: https://lnkd.in/gpwnpK8M
📌 Rotate List: https://lnkd.in/gKg_3D34
Hard:
📌 Merge k Sorted Lists: https://lnkd.in/g7wK8H8v
📌 Reverse Nodes in k-Group: https://lnkd.in/gUNaexhD
📌 Copy List with Random Pointer: https://lnkd.in/gBWcFRKe
📌 LRU Cache: https://lnkd.in/gURyUMZK
📌 Flatten a Multilevel Doubly Linked List: https://lnkd.in/gCtgKNwn
By understanding the answers to these questions, you can build a solid foundation for solving LinkedList-related problems and tackling algorithmic challenges!
To help you discover job openings and resources, I have created a dedicated Job Posting Community. It's a great place to stay updated on the latest job opportunities!
Join Here : https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Easy:
📌 Reverse Linked List: https://lnkd.in/g7qP9-YU
📌 Merge Two Sorted Lists: https://lnkd.in/gRfC6yyF
📌 Remove Nth Node From End of List: https://lnkd.in/gGnGF75X
📌 Delete Node in a Linked List: https://lnkd.in/gqzDgFpN
📌 Palindrome Linked List: https://lnkd.in/gmEjY4gr
Medium:
📌 Add Two Numbers: https://lnkd.in/gvDxHySa
📌 Swap Nodes in Pairs: https://lnkd.in/gnhqwidB
📌 Odd Even Linked List: https://lnkd.in/gS2QpJAw
📌 Intersection of Two Linked Lists: https://lnkd.in/gpwnpK8M
📌 Rotate List: https://lnkd.in/gKg_3D34
Hard:
📌 Merge k Sorted Lists: https://lnkd.in/g7wK8H8v
📌 Reverse Nodes in k-Group: https://lnkd.in/gUNaexhD
📌 Copy List with Random Pointer: https://lnkd.in/gBWcFRKe
📌 LRU Cache: https://lnkd.in/gURyUMZK
📌 Flatten a Multilevel Doubly Linked List: https://lnkd.in/gCtgKNwn
By understanding the answers to these questions, you can build a solid foundation for solving LinkedList-related problems and tackling algorithmic challenges!
To help you discover job openings and resources, I have created a dedicated Job Posting Community. It's a great place to stay updated on the latest job opportunities!
Join Here : https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👍4
30-days learning plan to master Data Structures and Algorithms (DSA) and prepare for coding interviews.
### Week 1: Foundations and Basic Data Structures
Day 1-3: Arrays and Strings
- Topics to Cover:
- Array basics, operations (insertion, deletion, searching)
- String manipulation
- Two-pointer technique, sliding window technique
- Practice Problems:
- Two Sum
- Maximum Subarray
- Reverse a String
- Longest Substring Without Repeating Characters
Day 4-5: Linked Lists
- Topics to Cover:
- Singly linked list, doubly linked list, circular linked list
- Common operations (insertion, deletion, reversal)
- Practice Problems:
- Reverse a Linked List
- Merge Two Sorted Lists
- Remove Nth Node From End of List
Day 6-7: Stacks and Queues
- Topics to Cover:
- Stack operations (push, pop, top)
- Queue operations (enqueue, dequeue)
- Applications (expression evaluation, backtracking, breadth-first search)
- Practice Problems:
- Valid Parentheses
- Implement Stack using Queues
- Implement Queue using Stacks
### Week 2: Advanced Data Structures
Day 8-10: Trees
- Topics to Cover:
- Binary Trees, Binary Search Trees (BST)
- Tree traversal (preorder, inorder, postorder, level order)
- Practice Problems:
- Invert Binary Tree
- Validate Binary Search Tree
- Serialize and Deserialize Binary Tree
Day 11-13: Heaps and Priority Queues
- Topics to Cover:
- Binary heap (min-heap, max-heap)
- Heap operations (insert, delete, extract-min/max)
- Applications (heap sort, priority queues)
- Practice Problems:
- Kth Largest Element in an Array
- Top K Frequent Elements
- Find Median from Data Stream
Day 14: Hash Tables
- Topics to Cover:
- Hashing concept, hash functions, collision resolution (chaining, open addressing)
- Applications (caching, counting frequencies)
- Practice Problems:
- Two Sum (using hash map)
- Group Anagrams
- Subarray Sum Equals K
### Week 3: Algorithms
Day 15-17: Sorting and Searching Algorithms
- Topics to Cover:
- Sorting algorithms (quick sort, merge sort, bubble sort, insertion sort)
- Searching algorithms (binary search, linear search)
- Practice Problems:
- Merge Intervals
- Search in Rotated Sorted Array
- Sort Colors
- Find Peak Element
Day 18-20: Recursion and Backtracking
- Topics to Cover:
- Basic recursion, tail recursion
- Backtracking (N-Queens, Sudoku solver)
- Practice Problems:
- Permutations
- Combination Sum
- Subsets
- Word Search
Day 21: Divide and Conquer
- Topics to Cover:
- Basic concept, merge sort, quick sort, binary search
- Practice Problems:
- Median of Two Sorted Arrays
- Pow(x, n)
- Kth Largest Element in an Array (using divide and conquer)
- Maximum Subarray (using divide and conquer)
### Week 4: Graphs and Dynamic Programming
Day 22-24: Graphs
- Topics to Cover:
- Graph representations (adjacency list, adjacency matrix)
- Traversal algorithms (DFS, BFS)
- Shortest path algorithms (Dijkstra's, Bellman-Ford)
- Practice Problems:
- Number of Islands
Day 25-27: Dynamic Programming
- Topics to Cover:
- Basic concept, memoization, tabulation
- Common problems (knapsack, longest common subsequence)
- Practice Problems:
- Longest Increasing Subsequence
- Maximum Product Subarray
Day 28: Advanced Topics and Miscellaneous
- Topics to Cover:
- Bit manipulation
- Greedy algorithms
- Miscellaneous problems (trie, segment tree, disjoint set)
- Practice Problems:
- Single Number
- Decode Ways
- Minimum Spanning Tree
### Week 5: Review and Mock Interviews
Day 29: Review and Weakness Analysis
- Activities:
- Review topics you found difficult
- Revisit problems you struggled with
Day 30: Mock Interviews and Practice
- Activities:
- Conduct mock interviews with a friend or use online platforms
- Focus on communication and explaining your thought process
Top DSA resources to crack coding interview
👉 GeekforGeeks
👉 Leetcode
👉 DSA Steps
👉 FreeCodeCamp
👉 Coding Interviews
👉 Best DSA Resources
Join for more: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Foundations and Basic Data Structures
Day 1-3: Arrays and Strings
- Topics to Cover:
- Array basics, operations (insertion, deletion, searching)
- String manipulation
- Two-pointer technique, sliding window technique
- Practice Problems:
- Two Sum
- Maximum Subarray
- Reverse a String
- Longest Substring Without Repeating Characters
Day 4-5: Linked Lists
- Topics to Cover:
- Singly linked list, doubly linked list, circular linked list
- Common operations (insertion, deletion, reversal)
- Practice Problems:
- Reverse a Linked List
- Merge Two Sorted Lists
- Remove Nth Node From End of List
Day 6-7: Stacks and Queues
- Topics to Cover:
- Stack operations (push, pop, top)
- Queue operations (enqueue, dequeue)
- Applications (expression evaluation, backtracking, breadth-first search)
- Practice Problems:
- Valid Parentheses
- Implement Stack using Queues
- Implement Queue using Stacks
### Week 2: Advanced Data Structures
Day 8-10: Trees
- Topics to Cover:
- Binary Trees, Binary Search Trees (BST)
- Tree traversal (preorder, inorder, postorder, level order)
- Practice Problems:
- Invert Binary Tree
- Validate Binary Search Tree
- Serialize and Deserialize Binary Tree
Day 11-13: Heaps and Priority Queues
- Topics to Cover:
- Binary heap (min-heap, max-heap)
- Heap operations (insert, delete, extract-min/max)
- Applications (heap sort, priority queues)
- Practice Problems:
- Kth Largest Element in an Array
- Top K Frequent Elements
- Find Median from Data Stream
Day 14: Hash Tables
- Topics to Cover:
- Hashing concept, hash functions, collision resolution (chaining, open addressing)
- Applications (caching, counting frequencies)
- Practice Problems:
- Two Sum (using hash map)
- Group Anagrams
- Subarray Sum Equals K
### Week 3: Algorithms
Day 15-17: Sorting and Searching Algorithms
- Topics to Cover:
- Sorting algorithms (quick sort, merge sort, bubble sort, insertion sort)
- Searching algorithms (binary search, linear search)
- Practice Problems:
- Merge Intervals
- Search in Rotated Sorted Array
- Sort Colors
- Find Peak Element
Day 18-20: Recursion and Backtracking
- Topics to Cover:
- Basic recursion, tail recursion
- Backtracking (N-Queens, Sudoku solver)
- Practice Problems:
- Permutations
- Combination Sum
- Subsets
- Word Search
Day 21: Divide and Conquer
- Topics to Cover:
- Basic concept, merge sort, quick sort, binary search
- Practice Problems:
- Median of Two Sorted Arrays
- Pow(x, n)
- Kth Largest Element in an Array (using divide and conquer)
- Maximum Subarray (using divide and conquer)
### Week 4: Graphs and Dynamic Programming
Day 22-24: Graphs
- Topics to Cover:
- Graph representations (adjacency list, adjacency matrix)
- Traversal algorithms (DFS, BFS)
- Shortest path algorithms (Dijkstra's, Bellman-Ford)
- Practice Problems:
- Number of Islands
Day 25-27: Dynamic Programming
- Topics to Cover:
- Basic concept, memoization, tabulation
- Common problems (knapsack, longest common subsequence)
- Practice Problems:
- Longest Increasing Subsequence
- Maximum Product Subarray
Day 28: Advanced Topics and Miscellaneous
- Topics to Cover:
- Bit manipulation
- Greedy algorithms
- Miscellaneous problems (trie, segment tree, disjoint set)
- Practice Problems:
- Single Number
- Decode Ways
- Minimum Spanning Tree
### Week 5: Review and Mock Interviews
Day 29: Review and Weakness Analysis
- Activities:
- Review topics you found difficult
- Revisit problems you struggled with
Day 30: Mock Interviews and Practice
- Activities:
- Conduct mock interviews with a friend or use online platforms
- Focus on communication and explaining your thought process
Top DSA resources to crack coding interview
👉 GeekforGeeks
👉 Leetcode
👉 DSA Steps
👉 FreeCodeCamp
👉 Coding Interviews
👉 Best DSA Resources
Join for more: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
👍11❤2
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
👍9❤2👏1👌1
DSA question by understanding patterns
If the input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/ subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
- QuickSelect
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
𝐉𝐨𝐢𝐧 𝐭𝐡𝐢𝐬 𝐭𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐠𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐉𝐨𝐛𝐬/Notes: https://t.me/getjobss
If the input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/ subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
- QuickSelect
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
𝐉𝐨𝐢𝐧 𝐭𝐡𝐢𝐬 𝐭𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐠𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐉𝐨𝐛𝐬/Notes: https://t.me/getjobss
👍10
Step-by-Step Approach
➊ Learn One Programming Language
↓
➋ Fundamentals → Time & Space Complexity
↓
➌ Brute Force Algorithms
↓
➍ Basic Data Structures → Array, Linked List
↓
➎ Simple Search Algorithm
↓
➏ Sorting Techniques → Bubble, Selection, Insertion
↓
➐ Slightly Complex Algorithms → Recursion, DnC
↓
➑ Complex Data Structures → Stack, Queue, Tree
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
➊ Learn One Programming Language
↓
➋ Fundamentals → Time & Space Complexity
↓
➌ Brute Force Algorithms
↓
➍ Basic Data Structures → Array, Linked List
↓
➎ Simple Search Algorithm
↓
➏ Sorting Techniques → Bubble, Selection, Insertion
↓
➐ Slightly Complex Algorithms → Recursion, DnC
↓
➑ Complex Data Structures → Stack, Queue, Tree
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
👍7❤1🥰1
Steps to learn Data Structures and Algorithms (DSA) with Python
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍13❤1
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 you’ll 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 it’s 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 you’ll not have any noise distractions.
5. For Zoom/Skype calls, use your desktop or laptop instead of your phone. They’re 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 it’s 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 you’ll 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 it’s 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 you’ll not have any noise distractions.
5. For Zoom/Skype calls, use your desktop or laptop instead of your phone. They’re 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 it’s 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.
👍11
✅Meta interview questions : Most asked in last 30 days
1. 1249. Minimum Remove to Make Valid Parentheses
2. 408. Valid Word Abbreviation
3. 215. Kth Largest Element in an Array
4. 314. Binary Tree Vertical Order Traversal
5. 88. Merge Sorted Array
6. 339. Nested List Weight Sum
7. 680. Valid Palindrome II
8. 973. K Closest Points to Origin
9. 1650. Lowest Common Ancestor of a Binary Tree III
10. 1. Two Sum
11. 791. Custom Sort String
12. 56. Merge Intervals
13. 528. Random Pick with Weight
14. 1570. Dot Product of Two Sparse Vectors
15. 50. Pow(x, n)
16. 65. Valid Number
17. 227. Basic Calculator II
18. 560. Subarray Sum Equals K
19. 71. Simplify Path
20. 200. Number of Islands
21. 236. Lowest Common Ancestor of a Binary Tree
22. 347. Top K Frequent Elements
23. 498. Diagonal Traverse
24. 543. Diameter of Binary Tree
25. 1768. Merge Strings Alternately
26. 2. Add Two Numbers
27. 4. Median of Two Sorted Arrays
28. 7. Reverse Integer
29. 31. Next Permutation
30. 34. Find First and Last Position of Element in Sorted Array
31. 84. Largest Rectangle in Histogram
32. 146. LRU Cache
33. 162. Find Peak Element
34. 199. Binary Tree Right Side View
35. 938. Range Sum of BST
36. 17. Letter Combinations of a Phone Number
37. 125. Valid Palindrome
38. 153. Find Minimum in Rotated Sorted Array
39. 283. Move Zeroes
40. 523. Continuous Subarray Sum
41. 658. Find K Closest Elements
42. 670. Maximum Swap
43. 827. Making A Large Island
44. 987. Vertical Order Traversal of a Binary Tree
45. 1757. Recyclable and Low Fat Products
46. 1762. Buildings With an Ocean View
47. 2667. Create Hello World Function
48. 5. Longest Palindromic Substring
49. 15. 3Sum
50. 19. Remove Nth Node From End of List
51. 70. Climbing Stairs
52. 80. Remove Duplicates from Sorted Array II
53. 113. Path Sum II
54. 121. Best Time to Buy and Sell Stock
55. 127. Word Ladder
56. 128. Longest Consecutive Sequence
57. 133. Clone Graph
58. 138. Copy List with Random Pointer
59. 140. Word Break II
60. 142. Linked List Cycle II
61. 145. Binary Tree Postorder Traversal
62. 173. Binary Search Tree Iterator
63. 206. Reverse Linked List
64. 207. Course Schedule
65. 394. Decode String
66. 415. Add Strings
67. 437. Path Sum III
68. 468. Validate IP Address
70. 691. Stickers to Spell Word
71. 725. Split Linked List in Parts
72. 766. Toeplitz Matrix
73. 708. Insert into a Sorted Circular Linked List
74. 1091. Shortest Path in Binary Matrix
75. 1514. Path with Maximum Probability
76. 1609. Even Odd Tree
77. 1868. Product of Two Run-Length Encoded Arrays
78. 2022. Convert 1D Array Into 2D Array
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
1. 1249. Minimum Remove to Make Valid Parentheses
2. 408. Valid Word Abbreviation
3. 215. Kth Largest Element in an Array
4. 314. Binary Tree Vertical Order Traversal
5. 88. Merge Sorted Array
6. 339. Nested List Weight Sum
7. 680. Valid Palindrome II
8. 973. K Closest Points to Origin
9. 1650. Lowest Common Ancestor of a Binary Tree III
10. 1. Two Sum
11. 791. Custom Sort String
12. 56. Merge Intervals
13. 528. Random Pick with Weight
14. 1570. Dot Product of Two Sparse Vectors
15. 50. Pow(x, n)
16. 65. Valid Number
17. 227. Basic Calculator II
18. 560. Subarray Sum Equals K
19. 71. Simplify Path
20. 200. Number of Islands
21. 236. Lowest Common Ancestor of a Binary Tree
22. 347. Top K Frequent Elements
23. 498. Diagonal Traverse
24. 543. Diameter of Binary Tree
25. 1768. Merge Strings Alternately
26. 2. Add Two Numbers
27. 4. Median of Two Sorted Arrays
28. 7. Reverse Integer
29. 31. Next Permutation
30. 34. Find First and Last Position of Element in Sorted Array
31. 84. Largest Rectangle in Histogram
32. 146. LRU Cache
33. 162. Find Peak Element
34. 199. Binary Tree Right Side View
35. 938. Range Sum of BST
36. 17. Letter Combinations of a Phone Number
37. 125. Valid Palindrome
38. 153. Find Minimum in Rotated Sorted Array
39. 283. Move Zeroes
40. 523. Continuous Subarray Sum
41. 658. Find K Closest Elements
42. 670. Maximum Swap
43. 827. Making A Large Island
44. 987. Vertical Order Traversal of a Binary Tree
45. 1757. Recyclable and Low Fat Products
46. 1762. Buildings With an Ocean View
47. 2667. Create Hello World Function
48. 5. Longest Palindromic Substring
49. 15. 3Sum
50. 19. Remove Nth Node From End of List
51. 70. Climbing Stairs
52. 80. Remove Duplicates from Sorted Array II
53. 113. Path Sum II
54. 121. Best Time to Buy and Sell Stock
55. 127. Word Ladder
56. 128. Longest Consecutive Sequence
57. 133. Clone Graph
58. 138. Copy List with Random Pointer
59. 140. Word Break II
60. 142. Linked List Cycle II
61. 145. Binary Tree Postorder Traversal
62. 173. Binary Search Tree Iterator
63. 206. Reverse Linked List
64. 207. Course Schedule
65. 394. Decode String
66. 415. Add Strings
67. 437. Path Sum III
68. 468. Validate IP Address
70. 691. Stickers to Spell Word
71. 725. Split Linked List in Parts
72. 766. Toeplitz Matrix
73. 708. Insert into a Sorted Circular Linked List
74. 1091. Shortest Path in Binary Matrix
75. 1514. Path with Maximum Probability
76. 1609. Even Odd Tree
77. 1868. Product of Two Run-Length Encoded Arrays
78. 2022. Convert 1D Array Into 2D Array
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
👍9
ChatGPT can help you land your dream job twice as fast. Here are 8 powerful ChatGPT prompts will 10X your interview chances.
Free book to master ChatGPT: https://t.me/InterviewBooks/166
1. Customizing Your Resume ChatGPT prompt: "Can you make changes to my resume to fit the [Job Title] role at [Company]? Here's the job description: [Paste Job Description], and resume: [Paste Resume]."
2. Creating a Professional Summary ChatGPT prompt: "Using my resume, can you create a professional summary for me aligned to this [Job Title]." [Paste Resume]
3. Understanding Job Descriptions ChatGPT prompt: "What are the main responsibilities for this job? Please list the top three responsibilities required for [Job Title]." [Paste Job Description]
4. Improving Your Resume Bullets ChatGPT prompt: "Please rewrite this bullet point from my resume using clear and impactful language while highlighting my accomplishments. [Paste Resume]"
5. Writing a LinkedIn Summary ChatGPT prompt: "Can you write a summary for my LinkedIn profile using my resume [Paste Resume]?"
6. Job Applications with ChatGPT ChatGPT prompt: "Can you identify my [Skills] experience from my resume [Paste Resume]? Please describe my specific [Skills] experience in conversational, clear language as if you were me."
7. Crafting Your Cover Letter ChatGPT prompt: "Can you write a personalized cover letter for the [Job Title] position at [Company]? Here's the job description: [Paste Job Description], and my current resume: [Paste Resume]."
8. Preparing for Interviews ChatGPT prompt: "What skills and experiences should I emphasize during an interview for the [Job Title] role in [Specific Industry]?"
ENJOY LEARNING 👍👍
Free book to master ChatGPT: https://t.me/InterviewBooks/166
1. Customizing Your Resume ChatGPT prompt: "Can you make changes to my resume to fit the [Job Title] role at [Company]? Here's the job description: [Paste Job Description], and resume: [Paste Resume]."
2. Creating a Professional Summary ChatGPT prompt: "Using my resume, can you create a professional summary for me aligned to this [Job Title]." [Paste Resume]
3. Understanding Job Descriptions ChatGPT prompt: "What are the main responsibilities for this job? Please list the top three responsibilities required for [Job Title]." [Paste Job Description]
4. Improving Your Resume Bullets ChatGPT prompt: "Please rewrite this bullet point from my resume using clear and impactful language while highlighting my accomplishments. [Paste Resume]"
5. Writing a LinkedIn Summary ChatGPT prompt: "Can you write a summary for my LinkedIn profile using my resume [Paste Resume]?"
6. Job Applications with ChatGPT ChatGPT prompt: "Can you identify my [Skills] experience from my resume [Paste Resume]? Please describe my specific [Skills] experience in conversational, clear language as if you were me."
7. Crafting Your Cover Letter ChatGPT prompt: "Can you write a personalized cover letter for the [Job Title] position at [Company]? Here's the job description: [Paste Job Description], and my current resume: [Paste Resume]."
8. Preparing for Interviews ChatGPT prompt: "What skills and experiences should I emphasize during an interview for the [Job Title] role in [Specific Industry]?"
ENJOY LEARNING 👍👍
❤1👍1
Shoutout to all the late-night coders—may your coffee be strong and your bugs be few!
👏11👍4❤2
Best way to prepare for Python interviews 👇👇
1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python Course with FREE Certificate
Python for Data Analysis and Visualization
Python course for beginners by Microsoft
Python course by Google
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python Course with FREE Certificate
Python for Data Analysis and Visualization
Python course for beginners by Microsoft
Python course by Google
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
👍5❤1
New developers: Whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.)
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
❤4
Top 21 skills to learn this year 👇
1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.
Join for more: 👇
https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.
Join for more: 👇
https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
👍6