Few common problems with lot of resumes:
1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
❤7
🧩 Core Computer Science Concepts
🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design
React ❤️ for more like this
🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design
React ❤️ for more like this
❤8🥰1
𝗜𝗻𝗱𝗶𝗮’𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 | 𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗕𝘂𝗶𝗹𝗱𝗮𝘁𝗵𝗼𝗻😍
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
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a flagship initiative of the Government of India 🇮🇳
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
❤1
✅ 🔤 A–Z of Full Stack Development
A – Authentication
Verifying user identity using methods like login, tokens, or biometrics.
B – Build Tools
Automate tasks like bundling, transpiling, and optimizing code (e.g., Webpack, Vite).
C – CRUD
Create, Read, Update, Delete – the core operations of most web apps.
D – Deployment
Publishing your app to a live server or cloud platform.
E – Environment Variables
Store sensitive data like API keys securely outside your codebase.
F – Frameworks
Tools that simplify development (e.g., React, Express, Django).
G – GraphQL
A query language for APIs that gives clients exactly the data they need.
H – HTTP (HyperText Transfer Protocol)
Foundation of data communication on the web.
I – Integration
Connecting different systems or services (e.g., payment gateways, APIs).
J – JWT (JSON Web Token)
Compact way to securely transmit information between parties for authentication.
K – Kubernetes
Tool for automating deployment and scaling of containerized applications.
L – Load Balancer
Distributes incoming traffic across multiple servers for better performance.
M – Middleware
Functions that run during request/response cycles in backend frameworks.
N – NPM (Node Package Manager)
Tool to manage JavaScript packages and dependencies.
O – ORM (Object-Relational Mapping)
Maps database tables to objects in code (e.g., Sequelize, Prisma).
P – PostgreSQL
Powerful open-source relational database system.
Q – Queue
Used for handling background tasks (e.g., RabbitMQ, Redis queues).
R – REST API
Architectural style for designing networked applications using HTTP.
S – Sessions
Store user data across multiple requests (e.g., login sessions).
T – Testing
Ensures your code works as expected (e.g., Jest, Mocha, Cypress).
U – UX (User Experience)
Designing intuitive and enjoyable user interactions.
V – Version Control
Track and manage code changes (e.g., Git, GitHub).
W – WebSockets
Enable real-time communication between client and server.
X – XSS (Cross-Site Scripting)
Security vulnerability where attackers inject malicious noscripts into web pages.
Y – YAML
Human-readable data format often used for configuration files.
Z – Zero Downtime Deployment
Deploy updates without interrupting the running application.
💬 Double Tap ❤️ for more!
A – Authentication
Verifying user identity using methods like login, tokens, or biometrics.
B – Build Tools
Automate tasks like bundling, transpiling, and optimizing code (e.g., Webpack, Vite).
C – CRUD
Create, Read, Update, Delete – the core operations of most web apps.
D – Deployment
Publishing your app to a live server or cloud platform.
E – Environment Variables
Store sensitive data like API keys securely outside your codebase.
F – Frameworks
Tools that simplify development (e.g., React, Express, Django).
G – GraphQL
A query language for APIs that gives clients exactly the data they need.
H – HTTP (HyperText Transfer Protocol)
Foundation of data communication on the web.
I – Integration
Connecting different systems or services (e.g., payment gateways, APIs).
J – JWT (JSON Web Token)
Compact way to securely transmit information between parties for authentication.
K – Kubernetes
Tool for automating deployment and scaling of containerized applications.
L – Load Balancer
Distributes incoming traffic across multiple servers for better performance.
M – Middleware
Functions that run during request/response cycles in backend frameworks.
N – NPM (Node Package Manager)
Tool to manage JavaScript packages and dependencies.
O – ORM (Object-Relational Mapping)
Maps database tables to objects in code (e.g., Sequelize, Prisma).
P – PostgreSQL
Powerful open-source relational database system.
Q – Queue
Used for handling background tasks (e.g., RabbitMQ, Redis queues).
R – REST API
Architectural style for designing networked applications using HTTP.
S – Sessions
Store user data across multiple requests (e.g., login sessions).
T – Testing
Ensures your code works as expected (e.g., Jest, Mocha, Cypress).
U – UX (User Experience)
Designing intuitive and enjoyable user interactions.
V – Version Control
Track and manage code changes (e.g., Git, GitHub).
W – WebSockets
Enable real-time communication between client and server.
X – XSS (Cross-Site Scripting)
Security vulnerability where attackers inject malicious noscripts into web pages.
Y – YAML
Human-readable data format often used for configuration files.
Z – Zero Downtime Deployment
Deploy updates without interrupting the running application.
💬 Double Tap ❤️ for more!
❤10
🚀 𝟰 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟲 😍
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❤3
Roadmap to Become Web3 Developer :
📂 Learn HTML
∟📂 Learn CSS
∟📂 Learn JavaScript
∟📂 Learn React
∟📂 Learn Solidity
∟📂 Learn Ether.js
∟📂 Learn L2
∟📂 Build Projects
∟ ✅ Apply For Job
React ❤️ for More 👨💻
📂 Learn HTML
∟📂 Learn CSS
∟📂 Learn JavaScript
∟📂 Learn React
∟📂 Learn Solidity
∟📂 Learn Ether.js
∟📂 Learn L2
∟📂 Build Projects
∟ ✅ Apply For Job
React ❤️ for More 👨💻
❤6
𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 😍
* JAVA- Full Stack Development With Gen AI
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Highlightes:-
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𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
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Hurry, limited seats available!
* JAVA- Full Stack Development With Gen AI
* MERN- Full Stack Development With Gen AI
Highlightes:-
* 2000+ Students Placed
* Attend FREE Hiring Drives at our Skill Centres
* Learn from India's Best Mentors
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
https://pdlink.in/4hO7rWY
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❤10
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✅ Limited seats only
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⏳ Deadline: 31st January 2026
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✅ Limited seats only
❤1
These are top 5 data structures and algorithms projects, allowing you to dive deep into the world of DSA 💪🏻
•Project 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
•Project 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
•Project 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
•Project 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
•Project 5: Map Navigator (Dijkstra’s
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstra’s algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
You can check these amazing resources for DSA Preparation
Join for more: https://news.1rj.ru/str/crackingthecodinginterview
All the best 👍👍
•Project 1: Snakes Game (Arrays)
The Snakes Game project is a classic implementation of the popular game
Snake.
This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.
•Project 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)
The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts
•Project 3: Sudoku Solver (Backtracking)
The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.
•Project 4: File Zipper (Greedy Huffman
Encoder)
The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.
•Project 5: Map Navigator (Dijkstra’s
Algorithm)
The Map Navigator project aims to develop a navigation system using Dijkstra’s algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.
You can check these amazing resources for DSA Preparation
Join for more: https://news.1rj.ru/str/crackingthecodinginterview
All the best 👍👍
❤3
Top Coding Domains You Should Explore in 2026 ✅
• Backend Development
Build server-side systems
Handle logic, databases, APIs
Core skills
Languages: Java, Python, Node.js
Databases: MySQL, PostgreSQL, MongoDB
APIs: REST, GraphQL
Auth, caching, scalability
Who fits: Strong logic, system thinking, long-term products
• Frontend Development
Build user interfaces
Focus on user experience
Core skills
HTML, CSS, JavaScript
React, Angular, Vue
State management, browser performance
Who fits: Visual thinkers, UI focus, fast feedback lovers
• Mobile App Development
Build Android and iOS apps
Core skills
Android: Kotlin, Java
iOS: Swift
Flutter, React Native
App lifecycle
Who fits: Mobile-first mindset, product builders, app store focus
• Data Analytics
Turn data into insights
Core skills
SQL, Excel
Python
Power BI, Tableau
Who fits: Business thinkers, numbers-driven minds, decision support roles
• Data Science and ML
Build predictive systems
Core skills
Python
Statistics
Machine learning
Pandas, NumPy, scikit-learn
Who fits: Math interest, research mindset, model builders
• DevOps and Cloud
Deploy and scale systems
Core skills
Linux
AWS, Azure, GCP
Docker, Kubernetes
CI/CD
Who fits: Automation lovers, system reliability focus, high-pressure roles
• Cybersecurity
Protect systems and data
Core skills
Networking
Linux
Security tools
Risk analysis
Who fits: Detail-oriented, defensive mindset, compliance roles
• Game Development
Build interactive games
Core skills
C++, C#
Unity, Unreal
Physics basics, game logic
Who fits: Creative coders, graphics interest, real-time systems
Best career advice
• Pick one domain
• Build real projects
• Learn tools used in jobs
• Switch later if needed
Which domain are you targeting next?
Development 👍
Data ❤️
DevOps/ Cybersecurity 🙏
Still exploring 🫡
• Backend Development
Build server-side systems
Handle logic, databases, APIs
Core skills
Languages: Java, Python, Node.js
Databases: MySQL, PostgreSQL, MongoDB
APIs: REST, GraphQL
Auth, caching, scalability
Who fits: Strong logic, system thinking, long-term products
• Frontend Development
Build user interfaces
Focus on user experience
Core skills
HTML, CSS, JavaScript
React, Angular, Vue
State management, browser performance
Who fits: Visual thinkers, UI focus, fast feedback lovers
• Mobile App Development
Build Android and iOS apps
Core skills
Android: Kotlin, Java
iOS: Swift
Flutter, React Native
App lifecycle
Who fits: Mobile-first mindset, product builders, app store focus
• Data Analytics
Turn data into insights
Core skills
SQL, Excel
Python
Power BI, Tableau
Who fits: Business thinkers, numbers-driven minds, decision support roles
• Data Science and ML
Build predictive systems
Core skills
Python
Statistics
Machine learning
Pandas, NumPy, scikit-learn
Who fits: Math interest, research mindset, model builders
• DevOps and Cloud
Deploy and scale systems
Core skills
Linux
AWS, Azure, GCP
Docker, Kubernetes
CI/CD
Who fits: Automation lovers, system reliability focus, high-pressure roles
• Cybersecurity
Protect systems and data
Core skills
Networking
Linux
Security tools
Risk analysis
Who fits: Detail-oriented, defensive mindset, compliance roles
• Game Development
Build interactive games
Core skills
C++, C#
Unity, Unreal
Physics basics, game logic
Who fits: Creative coders, graphics interest, real-time systems
Best career advice
• Pick one domain
• Build real projects
• Learn tools used in jobs
• Switch later if needed
Which domain are you targeting next?
Development 👍
Data ❤️
DevOps/ Cybersecurity 🙏
Still exploring 🫡
❤6👍1
🚀 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗪𝗶𝘁𝗵 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 (𝗘&𝗜𝗖𝗧 𝗔𝗰𝗮𝗱𝗲𝗺𝘆)
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⏳ Don’t miss this opportunity to upskill with IIT Roorkee.
✅ Top 50 DSA (Data Structures & Algorithms) Interview Questions 📚⚙️
1. What is a Data Structure?
2. What are the different types of data structures?
3. What is the difference between Array and Linked List?
4. How does a Stack work?
5. What is a Queue? Difference between Queue and Deque?
6. What is a Priority Queue?
7. What is a Hash Table and how does it work?
8. What is the difference between HashMap and HashSet?
9. What are Trees? Explain Binary Tree.
10. What is a Binary Search Tree (BST)?
11. What is the difference between BFS and DFS?
12. What is a Heap?
13. What is a Trie?
14. What is a Graph?
15. Difference between Directed and Undirected Graph?
16. What is the time complexity of common operations in arrays and linked lists?
17. What is recursion?
18. What are base case and recursive case?
19. What is dynamic programming?
20. Difference between Memoization and Tabulation?
21. What is the Sliding Window technique?
22. Explain Two-Pointer technique.
23. What is the Binary Search algorithm?
24. What is the Merge Sort algorithm?
25. What is the Quick Sort algorithm?
26. Difference between Merge Sort and Quick Sort?
27. What is Insertion Sort and how does it work?
28. What is Selection Sort?
29. What is Bubble Sort and its drawbacks?
30. What is the time and space complexity of sorting algorithms?
31. What is Backtracking?
32. Explain the N-Queens Problem.
33. What is the Kadane's Algorithm?
34. What is Floyd’s Cycle Detection Algorithm?
35. What is the Union-Find (Disjoint Set) algorithm?
36. What are topological sorting and its uses?
37. What is Dijkstra's Algorithm?
38. What is Bellman-Ford Algorithm?
39. What is Kruskal’s Algorithm?
40. What is Prim’s Algorithm?
41. What is Longest Common Subsequence (LCS)?
42. What is Longest Increasing Subsequence (LIS)?
43. What is a Palindrome Substring problem?
44. What is the difference between greedy and dynamic programming?
45. What is Big-O notation?
46. What is the difference between time and space complexity?
47. How to find the time complexity of a recursive function?
48. What are amortized time complexities?
49. What is tail recursion?
50. How do you approach solving a coding problem in interviews?
💬 Tap ❤️ for the detailed answers!
1. What is a Data Structure?
2. What are the different types of data structures?
3. What is the difference between Array and Linked List?
4. How does a Stack work?
5. What is a Queue? Difference between Queue and Deque?
6. What is a Priority Queue?
7. What is a Hash Table and how does it work?
8. What is the difference between HashMap and HashSet?
9. What are Trees? Explain Binary Tree.
10. What is a Binary Search Tree (BST)?
11. What is the difference between BFS and DFS?
12. What is a Heap?
13. What is a Trie?
14. What is a Graph?
15. Difference between Directed and Undirected Graph?
16. What is the time complexity of common operations in arrays and linked lists?
17. What is recursion?
18. What are base case and recursive case?
19. What is dynamic programming?
20. Difference between Memoization and Tabulation?
21. What is the Sliding Window technique?
22. Explain Two-Pointer technique.
23. What is the Binary Search algorithm?
24. What is the Merge Sort algorithm?
25. What is the Quick Sort algorithm?
26. Difference between Merge Sort and Quick Sort?
27. What is Insertion Sort and how does it work?
28. What is Selection Sort?
29. What is Bubble Sort and its drawbacks?
30. What is the time and space complexity of sorting algorithms?
31. What is Backtracking?
32. Explain the N-Queens Problem.
33. What is the Kadane's Algorithm?
34. What is Floyd’s Cycle Detection Algorithm?
35. What is the Union-Find (Disjoint Set) algorithm?
36. What are topological sorting and its uses?
37. What is Dijkstra's Algorithm?
38. What is Bellman-Ford Algorithm?
39. What is Kruskal’s Algorithm?
40. What is Prim’s Algorithm?
41. What is Longest Common Subsequence (LCS)?
42. What is Longest Increasing Subsequence (LIS)?
43. What is a Palindrome Substring problem?
44. What is the difference between greedy and dynamic programming?
45. What is Big-O notation?
46. What is the difference between time and space complexity?
47. How to find the time complexity of a recursive function?
48. What are amortized time complexities?
49. What is tail recursion?
50. How do you approach solving a coding problem in interviews?
💬 Tap ❤️ for the detailed answers!
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💼 Placement Assistance with Top Hiring Partners
📁 Real-world Case Studies & Capstone Projects
📜 Industry-recognized Certification
💰 High Salary Career Path in Analytics & Data Science
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇:-
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