Guys, Big Announcement!
We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️
I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.
Here’s what we’ll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️
I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.
Here’s what we’ll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
👍5
Business Analyst Interview Questions and Answers
👇👇
1. What is analysis in tableau?
Ans: Tableau comes with inbuilt features to analyze the data plotted on a chart. We have various tools such as adding an average line to the chart which tableau calculates itself after we drop the tool on the chart. Some other features include clustering, percentages, forming bands of a particular range and various other tools to explore and inspect data. All these tools are available in analyze tab on each sheet used to create any chart. The features become visible only when they are applicable to the worksheet.
2.How to create sets in tableau?
Ans: Sets are custom fields used to compare and ask questions about a subset of data. For creating a set on dimension, right-click on a dimension in data pane and select create -> set. In general tab select the fields that will be considered for computing the set. Specify the conditions to create set in conditions tab and you also have the option to select top N members in dataset based on any field in the top tab. When a set is created it divides the measure into two parts namely in and out of the set based on the conditions applied by the user.
3.Why and how would you use a custom visual file?
A custom visual file is used when none of the pre existing visuals fit the business needs. Custom visual files are generally created by Developers which can be used in the same way as prepackaged files.
4. What are the various type of users who can use Power BI?
Ans: PowerBI can be used by anyone for their requirements but there is a particular group of users who are more likely to use it:
Report Consumers: They consume the reports based on a specific information they need
Report Analyst: Report Analysts need detailed data for their analysis from the reports
Self Service Data Analyst: They are more experienced business data users. They have an in-depth understanding of the data to work with.
Basic Data Analyst: They can build their own datasets and are experienced in PowerBI Service
Advanced Data Analyst: They know how to write SQL Queries and have hands-on experience on PowerBI. They have experience in Advanced PowerBI with DAX training and data modelling.
👇👇
1. What is analysis in tableau?
Ans: Tableau comes with inbuilt features to analyze the data plotted on a chart. We have various tools such as adding an average line to the chart which tableau calculates itself after we drop the tool on the chart. Some other features include clustering, percentages, forming bands of a particular range and various other tools to explore and inspect data. All these tools are available in analyze tab on each sheet used to create any chart. The features become visible only when they are applicable to the worksheet.
2.How to create sets in tableau?
Ans: Sets are custom fields used to compare and ask questions about a subset of data. For creating a set on dimension, right-click on a dimension in data pane and select create -> set. In general tab select the fields that will be considered for computing the set. Specify the conditions to create set in conditions tab and you also have the option to select top N members in dataset based on any field in the top tab. When a set is created it divides the measure into two parts namely in and out of the set based on the conditions applied by the user.
3.Why and how would you use a custom visual file?
A custom visual file is used when none of the pre existing visuals fit the business needs. Custom visual files are generally created by Developers which can be used in the same way as prepackaged files.
4. What are the various type of users who can use Power BI?
Ans: PowerBI can be used by anyone for their requirements but there is a particular group of users who are more likely to use it:
Report Consumers: They consume the reports based on a specific information they need
Report Analyst: Report Analysts need detailed data for their analysis from the reports
Self Service Data Analyst: They are more experienced business data users. They have an in-depth understanding of the data to work with.
Basic Data Analyst: They can build their own datasets and are experienced in PowerBI Service
Advanced Data Analyst: They know how to write SQL Queries and have hands-on experience on PowerBI. They have experience in Advanced PowerBI with DAX training and data modelling.
❤2
Important Sorting Algorithms-
Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.
Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.
Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.
Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.
Important Searching Algorithms-
Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.
Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.
Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.
#coding
Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.
Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.
Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.
Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.
Important Searching Algorithms-
Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.
Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.
Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.
#coding
👍2
REST API – Essential Concepts 🚀
1️⃣ Fundamentals of REST API
REST (Representational State Transfer) – Architectural style for web services.
Statelessness – Each request is independent, no session stored on the server.
Client-Server Architecture – Separation of frontend and backend.
Cacheability – Responses can be cached for performance optimization.
2️⃣ HTTP Methods (CRUD Operations)
GET – Retrieve data (Read).
POST – Create new data (Create).
PUT – Update existing data (Update).
PATCH – Partially update data (Modify).
DELETE – Remove data (Delete).
3️⃣ API Endpoints & URL Structure
Resource Naming – Use plural nouns (/users, /orders).
Hierarchical Structure – Use nested URLs (/users/{id}/orders).
Query Parameters – Filter results (/products?category=electronics).
Path Parameters – Identify resources (/users/{id}).
4️⃣ Request & Response Format
JSON (JavaScript Object Notation) – Standard format for data exchange.
Headers – Define content type, authentication tokens.
Status Codes –
200 OK – Success.
201 Created – New resource created.
400 Bad Request – Invalid request.
401 Unauthorized – Authentication required.
403 Forbidden – Access denied.
404 Not Found – Resource doesn’t exist.
500 Internal Server Error – Server-side issue.
5️⃣ Authentication & Security
API Keys – Unique keys to access API.
OAuth 2.0 – Secure authorization framework.
JWT (JSON Web Tokens) – Token-based authentication.
Rate Limiting – Prevent API abuse.
CORS (Cross-Origin Resource Sharing) – Control resource access.
6️⃣ REST API Best Practices
Use Proper HTTP Methods – Follow standard conventions.
Handle Errors Gracefully – Return meaningful error messages.
Pagination – Limit data returned (/users?page=1&limit=10).
Versioning – Manage API versions (/api/v1/users).
Idempotency – Ensure repeated requests yield the same results.
7️⃣ Tools & Testing
Postman – API testing and debugging.
Swagger (OpenAPI) – API documentation and visualization.
cURL – Command-line API testing.
Web Development Free Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
ENJOY LEARNING 👍👍
1️⃣ Fundamentals of REST API
REST (Representational State Transfer) – Architectural style for web services.
Statelessness – Each request is independent, no session stored on the server.
Client-Server Architecture – Separation of frontend and backend.
Cacheability – Responses can be cached for performance optimization.
2️⃣ HTTP Methods (CRUD Operations)
GET – Retrieve data (Read).
POST – Create new data (Create).
PUT – Update existing data (Update).
PATCH – Partially update data (Modify).
DELETE – Remove data (Delete).
3️⃣ API Endpoints & URL Structure
Resource Naming – Use plural nouns (/users, /orders).
Hierarchical Structure – Use nested URLs (/users/{id}/orders).
Query Parameters – Filter results (/products?category=electronics).
Path Parameters – Identify resources (/users/{id}).
4️⃣ Request & Response Format
JSON (JavaScript Object Notation) – Standard format for data exchange.
Headers – Define content type, authentication tokens.
Status Codes –
200 OK – Success.
201 Created – New resource created.
400 Bad Request – Invalid request.
401 Unauthorized – Authentication required.
403 Forbidden – Access denied.
404 Not Found – Resource doesn’t exist.
500 Internal Server Error – Server-side issue.
5️⃣ Authentication & Security
API Keys – Unique keys to access API.
OAuth 2.0 – Secure authorization framework.
JWT (JSON Web Tokens) – Token-based authentication.
Rate Limiting – Prevent API abuse.
CORS (Cross-Origin Resource Sharing) – Control resource access.
6️⃣ REST API Best Practices
Use Proper HTTP Methods – Follow standard conventions.
Handle Errors Gracefully – Return meaningful error messages.
Pagination – Limit data returned (/users?page=1&limit=10).
Versioning – Manage API versions (/api/v1/users).
Idempotency – Ensure repeated requests yield the same results.
7️⃣ Tools & Testing
Postman – API testing and debugging.
Swagger (OpenAPI) – API documentation and visualization.
cURL – Command-line API testing.
Web Development Free Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
ENJOY LEARNING 👍👍
👍2
The reason you’re struggling with 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 interviews isn't because you haven’t studied countless case studies or designed multitude of systems.
It is because you haven't mastered the core concepts.
You can study all the complex systems, like distributed databases or scalable microservices architectures or designing a large-scale distributed system or a high-throughput messaging service. But these aren't as common as you might think.
Focus on strengthening your foundational knowledge first. No matter the language you prefer, whether Python, C++, Java, or JavaScript, start by building a solid understanding of core system design principles.
➤ System Design Key Concepts:
1. Scalability: https://lnkd.in/gpge_z76
2. Latency vs Throughput: https://lnkd.in/g_amhAtN
3. CAP Theorem: https://lnkd.in/g3hmVamx
4. ACID Transactions: https://lnkd.in/gMe2JqaF
5. Rate Limiting: https://lnkd.in/gWsTDR3m
6. API Design: https://lnkd.in/ghYzrr8q
7. Strong vs Eventual Consistency: https://lnkd.in/gJ-uXQXZ
8. Distributed Tracing: https://lnkd.in/d6r5RdXG
9. Synchronous vs. asynchronous communications: https://lnkd.in/gC3F2nvr
10. Batch Processing vs Stream Processing: https://lnkd.in/g4_MzM4s
11. Fault Tolerance: https://lnkd.in/dVJ6n3wA
➤ System Design Building Blocks:
1. Databases: https://lnkd.in/gti8gjpz
2. Horizontal vs Vertical Scaling: https://lnkd.in/gAH2e9du
3. Caching: https://lnkd.in/gC9piQbJ
4. Distributed Caching: https://lnkd.in/g7WKydNg
5. Load Balancing: https://lnkd.in/gQaa8sXK
6. SQL vs NoSQL: https://lnkd.in/g3WC_yxn
7. Database Scaling: https://lnkd.in/gAXpSyWQ
8. Data Replication: https://lnkd.in/gVAJxTpS
9. Data Redundancy: https://lnkd.in/gNN7TF7n
10. Database Sharding: https://lnkd.in/gMqqc6x9
11. Database Index's: https://lnkd.in/gCeshYVt
12. Proxy Server: https://lnkd.in/gi8KnKS6
13. WebSocket: https://lnkd.in/g76Gv2KQ
14. API Gateway: https://lnkd.in/gnsJGJaM
15. Message Queues: https://lnkd.in/gTzY6uk8
➤ System Design Architectural Patterns:
1. Event-Driven Architecture: https://lnkd.in/dp8CPvey
2. Client-Server Architecture: https://lnkd.in/dAARQYzq
3. Serverless Architecture: https://lnkd.in/gQNAXKkb
4. Microservices Architecture: https://lnkd.in/gFXUrz_T
➤ Machine Coding Round and Low Level Design Problems:
1. Design a Parking Lot: https://lnkd.in/dQaAuFd2
2. Design Splitwise: https://lnkd.in/dF5fBnex
3. Design Chess Validator: https://lnkd.in/dfAQHvN4
4. Design a Distributed Queue | Kafka: https://lnkd.in/dQ6_B4_M
5. Design Tic-Tac-Toe: https://lnkd.in/dFDApUBt
Python Interview Questions and Answers
https://t.iss.one/dsabooks/75
Beginner's guide for DSA
https://www.geeksforgeeks.org/the-ultimate-beginners-guide-for-dsa/amp/
Cracking the coding interview FREE BOOK
https://www.pdfdrive.com/cracking-the-coding-interview-189-programming-questions-and-solutions-d175292720.html
DSA Interview Questions and Answers
https://t.iss.one/crackingthecodinginterview/77
Data Science Interview Questions and Answers
https://t.iss.one/datasciencefun/958
Java Interview Questions with Answers
https://t.iss.one/Curiousprogrammer/106
➤ System Design and Architecture (HLD):
1. Design a Unique ID Generator Service
2. Design a URL Shortening Service
3. Design Whatsapp
4. Design Instagram/Twitter News Feed
5. Design Search Autocomplete | Design Typeahead
6. Design Zomato Restaurant Search | Design Proximity Service
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
It is because you haven't mastered the core concepts.
You can study all the complex systems, like distributed databases or scalable microservices architectures or designing a large-scale distributed system or a high-throughput messaging service. But these aren't as common as you might think.
Focus on strengthening your foundational knowledge first. No matter the language you prefer, whether Python, C++, Java, or JavaScript, start by building a solid understanding of core system design principles.
➤ System Design Key Concepts:
1. Scalability: https://lnkd.in/gpge_z76
2. Latency vs Throughput: https://lnkd.in/g_amhAtN
3. CAP Theorem: https://lnkd.in/g3hmVamx
4. ACID Transactions: https://lnkd.in/gMe2JqaF
5. Rate Limiting: https://lnkd.in/gWsTDR3m
6. API Design: https://lnkd.in/ghYzrr8q
7. Strong vs Eventual Consistency: https://lnkd.in/gJ-uXQXZ
8. Distributed Tracing: https://lnkd.in/d6r5RdXG
9. Synchronous vs. asynchronous communications: https://lnkd.in/gC3F2nvr
10. Batch Processing vs Stream Processing: https://lnkd.in/g4_MzM4s
11. Fault Tolerance: https://lnkd.in/dVJ6n3wA
➤ System Design Building Blocks:
1. Databases: https://lnkd.in/gti8gjpz
2. Horizontal vs Vertical Scaling: https://lnkd.in/gAH2e9du
3. Caching: https://lnkd.in/gC9piQbJ
4. Distributed Caching: https://lnkd.in/g7WKydNg
5. Load Balancing: https://lnkd.in/gQaa8sXK
6. SQL vs NoSQL: https://lnkd.in/g3WC_yxn
7. Database Scaling: https://lnkd.in/gAXpSyWQ
8. Data Replication: https://lnkd.in/gVAJxTpS
9. Data Redundancy: https://lnkd.in/gNN7TF7n
10. Database Sharding: https://lnkd.in/gMqqc6x9
11. Database Index's: https://lnkd.in/gCeshYVt
12. Proxy Server: https://lnkd.in/gi8KnKS6
13. WebSocket: https://lnkd.in/g76Gv2KQ
14. API Gateway: https://lnkd.in/gnsJGJaM
15. Message Queues: https://lnkd.in/gTzY6uk8
➤ System Design Architectural Patterns:
1. Event-Driven Architecture: https://lnkd.in/dp8CPvey
2. Client-Server Architecture: https://lnkd.in/dAARQYzq
3. Serverless Architecture: https://lnkd.in/gQNAXKkb
4. Microservices Architecture: https://lnkd.in/gFXUrz_T
➤ Machine Coding Round and Low Level Design Problems:
1. Design a Parking Lot: https://lnkd.in/dQaAuFd2
2. Design Splitwise: https://lnkd.in/dF5fBnex
3. Design Chess Validator: https://lnkd.in/dfAQHvN4
4. Design a Distributed Queue | Kafka: https://lnkd.in/dQ6_B4_M
5. Design Tic-Tac-Toe: https://lnkd.in/dFDApUBt
Python Interview Questions and Answers
https://t.iss.one/dsabooks/75
Beginner's guide for DSA
https://www.geeksforgeeks.org/the-ultimate-beginners-guide-for-dsa/amp/
Cracking the coding interview FREE BOOK
https://www.pdfdrive.com/cracking-the-coding-interview-189-programming-questions-and-solutions-d175292720.html
DSA Interview Questions and Answers
https://t.iss.one/crackingthecodinginterview/77
Data Science Interview Questions and Answers
https://t.iss.one/datasciencefun/958
Java Interview Questions with Answers
https://t.iss.one/Curiousprogrammer/106
➤ System Design and Architecture (HLD):
1. Design a Unique ID Generator Service
2. Design a URL Shortening Service
3. Design Whatsapp
4. Design Instagram/Twitter News Feed
5. Design Search Autocomplete | Design Typeahead
6. Design Zomato Restaurant Search | Design Proximity Service
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤2👍1
𝗛𝗼𝘄 𝘁𝗼 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 (𝗘𝘃𝗲𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲!)🚀
Breaking into tech without prior experience can feel impossible—especially when every posting demands what you don’t have: experience.
But here’s the truth: Skills > Experience (especially for interns).
Let’s break it down into a proven 6-step roadmap that actually works👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Build Core Skills (No CS Degree Needed!)
Start with the fundamentals:
✅ Choose one language: Python / JavaScript / C++
✅ Learn DSA basics: Arrays, Strings, Recursion, Hashmaps
✅ Explore either Web Dev (HTML, CSS, JS) or Backend (Node.js, Flask)
✅ Understand SQL + Git/GitHub for version control
🔹 𝗦𝘁𝗲𝗽 𝟮: Build Mini Projects (Your Real Resume!)
Internships look for what you can do, not just what you’ve learned. Build:
✅ A Portfolio Website (HTML, CSS, JS)
✅ A To-Do App (React + Firebase)
✅ A REST API (Node.js + MongoDB)
👉 One solid project > Dozens of certificates.
📍 Showcase it on GitHub and LinkedIn.
🔹 𝗦𝘁𝗲𝗽 𝟯: Contribute to Open Source (Get Real-World Exposure)
You don’t need a job to gain experience. Try:
✅ Beginner-friendly GitHub repos
✅ Fixing bugs, improving documentation
✅ Participating in Hacktoberfest, GirlScript, MLH
This builds confidence and credibility.
🔹 𝗦𝘁𝗲𝗽 𝟰: Optimize Resume & LinkedIn (Your Digital First Impression)
❌ No generic lines like “I’m passionate about coding”
✅ Highlight projects, GitHub links, and tech stack
✅ Use keywords like “Software Engineering Intern | JavaScript | SQL”
✅ Keep it concise—1 page is enough
📌 Stay active on GitHub + LinkedIn. Recruiters notice!
🔹 𝗦𝘁𝗲𝗽 𝟱: Apply Smart, Not Hard
Don’t just mass-apply. Be strategic:
✅ Check internship portals (Internshala, LinkedIn, AngelList)
✅ Explore company careers pages (TCS, Infosys, Amazon, startups)
✅ Reach out via referrals—network with seniors, alumni, or connections
💬 Try:
"Hi [Name], I admire your work at [Company]. I’ve been building skills in [Tech] and am seeking an internship. Are there any roles I could apply for?"
Networking opens doors applications can’t.
🔹 𝗦𝘁𝗲𝗽 𝟲:Ace the Interview (Preparation Beats Perfection)
✅ Know your resume inside-out
✅ Review basics of DSA, OOP, DBMS, OS
✅ Practice your intro—highlight projects + relevant skills
✅ Do mock interviews with peers or platforms like InterviewBit, Pramp
And if you’re rejected? Don’t stress. Ask for feedback and keep building.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 = 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵
No one starts perfect. Consistency beats credentials.
Start small, stay curious, and show up every day.
Let me know if you’re just getting started 👇
Web Development Resources ⬇️
https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
ENJOY LEARNING 👍👍
#webdevelopment
Breaking into tech without prior experience can feel impossible—especially when every posting demands what you don’t have: experience.
But here’s the truth: Skills > Experience (especially for interns).
Let’s break it down into a proven 6-step roadmap that actually works👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Build Core Skills (No CS Degree Needed!)
Start with the fundamentals:
✅ Choose one language: Python / JavaScript / C++
✅ Learn DSA basics: Arrays, Strings, Recursion, Hashmaps
✅ Explore either Web Dev (HTML, CSS, JS) or Backend (Node.js, Flask)
✅ Understand SQL + Git/GitHub for version control
🔹 𝗦𝘁𝗲𝗽 𝟮: Build Mini Projects (Your Real Resume!)
Internships look for what you can do, not just what you’ve learned. Build:
✅ A Portfolio Website (HTML, CSS, JS)
✅ A To-Do App (React + Firebase)
✅ A REST API (Node.js + MongoDB)
👉 One solid project > Dozens of certificates.
📍 Showcase it on GitHub and LinkedIn.
🔹 𝗦𝘁𝗲𝗽 𝟯: Contribute to Open Source (Get Real-World Exposure)
You don’t need a job to gain experience. Try:
✅ Beginner-friendly GitHub repos
✅ Fixing bugs, improving documentation
✅ Participating in Hacktoberfest, GirlScript, MLH
This builds confidence and credibility.
🔹 𝗦𝘁𝗲𝗽 𝟰: Optimize Resume & LinkedIn (Your Digital First Impression)
❌ No generic lines like “I’m passionate about coding”
✅ Highlight projects, GitHub links, and tech stack
✅ Use keywords like “Software Engineering Intern | JavaScript | SQL”
✅ Keep it concise—1 page is enough
📌 Stay active on GitHub + LinkedIn. Recruiters notice!
🔹 𝗦𝘁𝗲𝗽 𝟱: Apply Smart, Not Hard
Don’t just mass-apply. Be strategic:
✅ Check internship portals (Internshala, LinkedIn, AngelList)
✅ Explore company careers pages (TCS, Infosys, Amazon, startups)
✅ Reach out via referrals—network with seniors, alumni, or connections
💬 Try:
"Hi [Name], I admire your work at [Company]. I’ve been building skills in [Tech] and am seeking an internship. Are there any roles I could apply for?"
Networking opens doors applications can’t.
🔹 𝗦𝘁𝗲𝗽 𝟲:Ace the Interview (Preparation Beats Perfection)
✅ Know your resume inside-out
✅ Review basics of DSA, OOP, DBMS, OS
✅ Practice your intro—highlight projects + relevant skills
✅ Do mock interviews with peers or platforms like InterviewBit, Pramp
And if you’re rejected? Don’t stress. Ask for feedback and keep building.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 = 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵
No one starts perfect. Consistency beats credentials.
Start small, stay curious, and show up every day.
Let me know if you’re just getting started 👇
Web Development Resources ⬇️
https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
ENJOY LEARNING 👍👍
#webdevelopment
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🚀 Key Skills for Aspiring Tech Specialists
📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques
🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks
🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools
🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus
🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning
🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills
🔊 NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data
🌟 Embrace the world of data and AI, and become the architect of tomorrow's technology!
📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques
🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks
🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools
🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus
🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning
🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills
🔊 NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data
🌟 Embrace the world of data and AI, and become the architect of tomorrow's technology!
👍4❤1
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):
𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
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)
𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
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
𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
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
𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
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 👍👍
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):
𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
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)
𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
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
𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
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
𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
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 👍👍
👍5
What to do and What to avoid!
When sitting in front of an interviewer, your actions and words can make or break your chances.
It’s more than just answering questions, it's about presenting yourself as the ideal candidate.
Here are some clear do's and don'ts to keep in mind.
📌Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
📌Don't:
1. Don’t Fidget.
2. Don’t Speak Negatively About Past Employers.
3. Don’t Interrupt.
4. Don’t Overshare.
5. Don’t Forget to Follow Up.
By keeping these dos and don’ts in mind, you’ll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you 😊
When sitting in front of an interviewer, your actions and words can make or break your chances.
It’s more than just answering questions, it's about presenting yourself as the ideal candidate.
Here are some clear do's and don'ts to keep in mind.
📌Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
📌Don't:
1. Don’t Fidget.
2. Don’t Speak Negatively About Past Employers.
3. Don’t Interrupt.
4. Don’t Overshare.
5. Don’t Forget to Follow Up.
By keeping these dos and don’ts in mind, you’ll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you 😊
👍5❤1
Here's the sample answer to "Tell me about yourself?" according to the most common job roles👇👇
Frontend Developer-
Backend Developer-
Full-Stack Developer -
I hope you will find this helpful 🙌❤️
Frontend Developer-
Hi I’m [Your Name] and I'm a passionate front-end developer with [X years] of experience building user-friendly web interfaces. I'm proficient in HTML, CSS, and JavaScript, and I have a strong understanding of frameworks like React. I prioritize crafting clean, responsive code that delivers a seamless user experience.Backend Developer-
Hi I’m [Your Name] and I'm a skilled backend developer with a strong foundation in [mention your primary languages]. I possess expertise in server-side development, database management using SQL, and experience with frameworks like [mention relevant frameworks]. I enjoy tackling complex challenges and building robust, scalable back-end systems.Full-Stack Developer -
Hi I’m [Your Name] and I'm a passionate full-stack developer with [X years] of experience building web applications. I'm proficient in both front-end technologies like HTML, CSS, and JavaScript frameworks like [mention relevant ones]. I also have a strong understanding of back-end development using [mention languages like Python, Java] and frameworks like [mention relevant ones]. I enjoy tackling complex challenges and delivering user-centric solutions throughout the development cycle.
I hope you will find this helpful 🙌❤️
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Commonly asked System Design CONCEPT BASED interview topics -
1. Horizontal vs Vertical Partitioning:
Vertical partitioning splits tables by columns, often separating different features. Horizontal partitioning splits tables by rows, distributing data across multiple servers. Vertical organizes data logically, while horizontal improves scalability + performance.
2. Apache Kafka:
Kafka is a distributed streaming platform using a publish-subscribe model. It's fast due to the sequential disk I/O, zero-copy principle, and efficient batching of messages.
3. Rate Limiter:
A rate limiter controls the rate of requests a client can make to a service. It prevents overload and ensures fair resource usage.
4. JWT vs OAuth vs SAML:
JWT is a compact, self-contained token for secure information transmission. OAuth is an authorization framework for delegated access. SAML is an XML-based standard for exchanging authentication and authorization data.
5. Single Sign-On (SSO):
SSO allows users to access multiple applications with one set of credentials. It typically uses a central authentication server and protocols like SAML/OAuth.
6. Microservices vs Monolithic Architecture:
Microservices architecture breaks an application into small, independent services. Monolithic architecture is a single, tightly-coupled unit. Microservices offer scalability while monoliths are simpler to develop + deploy.
7. Reverse Proxy vs Forward Proxy:
A reverse proxy sits in front of web servers, forwarding client requests to backend servers. A forward proxy sits in front of clients, forwarding their requests to the internet. Reverse proxies are used for load balancing and security, while forward proxies are used for anonymity and filtering.
8. CAP Theorem:
The CAP theorem states that a distributed system can only provide two of three guarantees: Consistency, Availability, and Partition tolerance. In practice, partition tolerance is necessary, so systems must choose between consistency and availability during network partitions.
10. Efficient Caching Strategy:
Implement multi-level caching (browser, CDN, application server, database). Use appropriate cache invalidation strategies (TTL, event-based). Consider cache coherence for distributed systems.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Horizontal vs Vertical Partitioning:
Vertical partitioning splits tables by columns, often separating different features. Horizontal partitioning splits tables by rows, distributing data across multiple servers. Vertical organizes data logically, while horizontal improves scalability + performance.
2. Apache Kafka:
Kafka is a distributed streaming platform using a publish-subscribe model. It's fast due to the sequential disk I/O, zero-copy principle, and efficient batching of messages.
3. Rate Limiter:
A rate limiter controls the rate of requests a client can make to a service. It prevents overload and ensures fair resource usage.
4. JWT vs OAuth vs SAML:
JWT is a compact, self-contained token for secure information transmission. OAuth is an authorization framework for delegated access. SAML is an XML-based standard for exchanging authentication and authorization data.
5. Single Sign-On (SSO):
SSO allows users to access multiple applications with one set of credentials. It typically uses a central authentication server and protocols like SAML/OAuth.
6. Microservices vs Monolithic Architecture:
Microservices architecture breaks an application into small, independent services. Monolithic architecture is a single, tightly-coupled unit. Microservices offer scalability while monoliths are simpler to develop + deploy.
7. Reverse Proxy vs Forward Proxy:
A reverse proxy sits in front of web servers, forwarding client requests to backend servers. A forward proxy sits in front of clients, forwarding their requests to the internet. Reverse proxies are used for load balancing and security, while forward proxies are used for anonymity and filtering.
8. CAP Theorem:
The CAP theorem states that a distributed system can only provide two of three guarantees: Consistency, Availability, and Partition tolerance. In practice, partition tolerance is necessary, so systems must choose between consistency and availability during network partitions.
10. Efficient Caching Strategy:
Implement multi-level caching (browser, CDN, application server, database). Use appropriate cache invalidation strategies (TTL, event-based). Consider cache coherence for distributed systems.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
👍4❤1
Type of problem, while solving DSA problem in Array
❗ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:
𝟏. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐰𝐢𝐧𝐝𝐨𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.
𝟐. 𝐓𝐰𝐨 𝐩𝐨𝐢𝐧𝐭𝐞𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.
𝟑. 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.
𝟒. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.
𝟓. 𝐒𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.
𝟔. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.
❗ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:
𝟏. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐰𝐢𝐧𝐝𝐨𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.
𝟐. 𝐓𝐰𝐨 𝐩𝐨𝐢𝐧𝐭𝐞𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.
𝟑. 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.
𝟒. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.
𝟓. 𝐒𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.
𝟔. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.
👍2❤1
How to master Python from scratch🚀
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
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Frontend Development Interview Questions
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React ❤️ for the detailed answers
Join for free resources: 👇 https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React ❤️ for the detailed answers
Join for free resources: 👇 https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
👍2
Leetcode patterns you should definitely checkout to Learn DSA(Java) from scratch
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.iss.one/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.iss.one/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
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