𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗧𝗼𝗽 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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List of most asked Programming Interview Questions.

Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic.

Arrays

- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?

Linked Lists

- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?

Strings

- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?

Binary Trees

- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?

Graph

- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.

Dynamic Programming

1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?

𝟯𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗣 𝗟𝗜𝗙𝗘 𝘁𝗼 𝗦𝘂𝗽𝗲𝗿𝗰𝗵𝗮𝗿𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

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𝟲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Coding isn't easy!

It’s the art of turning ideas into functional, impactful software that shapes the world around us.

To truly excel in coding, focus on these key areas:

0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.


1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.


2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.


3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.


4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation scripts.


5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.


6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.


7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.


8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.


9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.

💡 Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.

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Back-end developer:

∟📂 Server-side coding (Python, Java, PHP, NodeJs, Go, .Net)
∟📂 Databases (SQL or NoSQL)
∟📂 APIs and web services
∟📂 Security and authentication
∟📂 Caching and performance tuning
∟📂 Scalability and load balancing
∟📂 Deployment and DevOps

Web Development – Essential Topics 🚀

1️⃣ HTML & CSS

Semantic HTML elements

CSS Flexbox & Grid for layout

Responsive design with media queries


2️⃣ JavaScript Fundamentals

ES6+ features (let, const, arrow functions, spread/rest operators)

DOM manipulation and event handling

Asynchronous JavaScript (Promises, async/await)


3️⃣ Frontend Frameworks & Libraries

React.js, Vue.js, or Angular

State management (Redux, Vuex)

Component-based architecture


4️⃣ Backend Development

Server-side languages (Node.js, Python, PHP)

RESTful APIs & GraphQL

Authentication & Authorization (JWT, OAuth)


5️⃣ Databases & Storage

SQL (MySQL, PostgreSQL) and NoSQL (MongoDB, Firebase)

Database design and normalization

CRUD operations and indexing


6️⃣ Version Control & Deployment

Git & GitHub for collaboration

CI/CD pipelines (GitHub Actions, Jenkins)

Hosting & deployment (Vercel, Netlify, AWS, Heroku)


7️⃣ Performance Optimization & Security

Lazy loading & caching techniques

Web security best practices (XSS, CSRF, CORS)

HTTPS, SSL, and Content Security Policy


8️⃣ Progressive Web Apps (PWAs) & Web3

Service workers & offline support

Web3 integration with blockchain & smart contracts

Decentralized applications (DApps)

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Web Development Essentials to build modern, responsive websites:

1. HTML (Structure)
Tags, Elements, and Attributes
Headings, Paragraphs, Lists
Forms, Inputs, Buttons
Images, Videos, Links
Semantic HTML: <header>, <nav>, <main>, <footer>

2. CSS (Styling)
Selectors, Properties, and Values
Box Model (margin, padding, border)
Flexbox & Grid Layout
Positioning (static, relative, absolute, fixed, sticky)
Media Queries (Responsive Design)

3. JavaScript (Interactivity)
Variables, Data Types, Operators
Functions, Conditionals, Loops
DOM Manipulation (getElementById, addEventListener)
Events (click, submit, change)
Arrays & Objects

4. Version Control (Git & GitHub)
Initialize repository, clone, commit, push, pull
Branching and merge conflicts
Hosting code on GitHub

5. Responsive Design
Mobile-first approach
Viewport meta tag
Flexbox and CSS Grid for layouts
Using relative units (%, em, rem)

6. Browser Dev Tools
Inspect elements
Console for debugging JavaScript
Network tab for API requests

7. Basic SEO & Accessibility
Title tags, meta descriptions
Alt attributes for images
Proper use of semantic tags

8. Deployment
Hosting on GitHub Pages, Netlify, or Vercel
Domain name basics
Continuous deployment setup

Web Development Resources ⬇️
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🧠 Technologies for Data Science, Machine Learning & AI!

📊 Data Science
▪️ Python – The go-to language for Data Science
▪️ R – Statistical Computing and Graphics
▪️ Pandas – Data Manipulation & Analysis
▪️ NumPy – Numerical Computing
▪️ Matplotlib / Seaborn – Data Visualization
▪️ Jupyter Notebooks – Interactive Development Environment

🤖 Machine Learning
▪️ Scikit-learn – Classical ML Algorithms
▪️ TensorFlow – Deep Learning Framework
▪️ Keras – High-Level Neural Networks API
▪️ PyTorch – Deep Learning with Dynamic Computation
▪️ XGBoost – High-Performance Gradient Boosting
▪️ LightGBM – Fast, Distributed Gradient Boosting

🧠 Artificial Intelligence
▪️ OpenAI GPT – Natural Language Processing
▪️ Transformers (Hugging Face) – Pretrained Models for NLP
▪️ spaCy – Industrial-Strength NLP
▪️ NLTK – Natural Language Toolkit
▪️ Computer Vision (OpenCV) – Image Processing & Object Detection
▪️ YOLO (You Only Look Once) – Real-Time Object Detection

💾 Data Storage & Databases
▪️ SQL – Structured Query Language for Databases
▪️ MongoDB – NoSQL, Flexible Data Storage
▪️ BigQuery – Google’s Data Warehouse for Large Scale Data
▪️ Apache Hadoop – Distributed Storage and Processing
▪️ Apache Spark – Big Data Processing & ML

🌐 Data Engineering & Deployment
▪️ Apache Airflow – Workflow Automation & Scheduling
▪️ Docker – Containerization for ML Models
▪️ Kubernetes – Container Orchestration
▪️ AWS Sagemaker / Google AI Platform – Cloud ML Model Deployment
▪️ Flask / FastAPI – APIs for ML Models

🔧 Tools & Libraries for Automation & Experimentation
▪️ MLflow – Tracking ML Experiments
▪️ TensorBoard – Visualization for TensorFlow Models
▪️ DVC (Data Version Control) – Versioning for Data & Models

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