Coding Projects

✅ Complete Roadmap to Learn SQL in 2026 🚀 💎 SQL powers 80% of data analytics jobs. 📚 🔹 SQL FOUNDATIONS 🎯 1️⃣ SELECT Basics (Week 1) - SELECT \*, specific columns - FROM tables - WHERE filters - ORDER BY, LIMIT 🟢 Practice: Query your first dataset today 🔍 2️⃣ Filtering Mastery - Comparison operators (=, >, BETWEEN) - Logical: AND, OR, IN - Pattern matching: LIKE, % - NULL handling 📊 3️⃣ Aggregate Power - COUNT(\*), SUM, AVG, MIN/MAX - GROUP BY essentials - HAVING vs WHERE - DISTINCT counts 🎓 🔥 SQL CORE SKILLS 🔗 4️⃣ JOINS (Most Important ⭐) - INNER JOIN (must-know) - LEFT, RIGHT, FULL JOIN - Multi-table joins - Self-joins ⚡ 5️⃣ Subqueries & CTEs - Subqueries in WHERE/FROM - WITH clause (CTEs) - Multiple CTE chains - EXISTS/NOT EXISTS 📈 6️⃣ Window Functions (Game-Changer ⭐) - ROW_NUMBER(), RANK() - PARTITION BY magic - LAG/LEAD (trends) - Running totals 🎨 🚀 ADVANCED SQL MASTERY ⏰ 7️⃣ Date & Time - DATEADD, DATEDIFF - DATE_TRUNC, EXTRACT - Date filtering patterns - Cohort analysis 🔤 8️⃣ String Functions - CONCAT, SUBSTRING - TRIM, UPPER/LOWER - LENGTH, REPLACE 🤖 9️⃣ CASE Statements - Simple vs searched CASE - Nested logic - Policy calculations ⚙️ 🔧 PERFORMANCE & JOBS 🚀 1️⃣0️⃣ Indexing Basics - CREATE INDEX strategies - EXPLAIN query plans - Composite indexes 💻 1️⃣1️⃣ Practice Platforms - LeetCode SQL (50 problems) - HackerRank SQL - StrataScratch (real cases) - DDIA datasets 📱 1️⃣2️⃣ Modern SQL Tools - pgAdmin (PostgreSQL) - DBeaver (universal) - BigQuery Sandbox (free) - dbt + SQL 💼 ⚡ INTERVIEW READY 🎯 1️⃣3️⃣ Top Interview Questions - Find 2nd highest salary - Nth highest records - Duplicate detection - Window ranking 📊 1️⃣4️⃣ Real Projects - Sales dashboard queries - Customer segmentation - Inventory optimization - Build GitHub portfolio 🎨 ⭐ ESSENTIAL SQL TOOLS 2026 - PostgreSQL (free, powerful) - MySQL Workbench - BigQuery (cloud-native) - Snowflake (trial) 1️⃣5️⃣ FREE RESOURCES 🌐 SQLBolt (interactive) 📚 Mode Analytics Tutorial ⚡ LeetCode SQL 50 🎥 DataCamp SQL (free tier) 🐙 W3schools Double Tap ♥️ For Detailed Explanation

🚀 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 - 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 If you’re serious about starting your career in tech, this is one opportunity you shouldn’t miss 🚀 ✅ 2000+ Students Already Placed 🤝 500+ Hiring Partners 💼 Salary: ₹7.4 LPA 🚀 Highest Package: ₹41 LPA 💻 Get trained in in-demand tech skills 👨‍🏫 Learn from industry experts 📈 Get dedicated placement support 💸 Pay only after you land a job 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-  https://pdlink.in/42WOE5H Hurry! Limited seats are available.🏃‍♂️

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✅ Coding Interview Acronyms You MUST Know 💻🔥 DSA → Data Structures & Algorithms CPU → Central Processing Unit RAM → Random Access Memory DBMS → Database Management System RDBMS → Relational Database Management System ACID → Atomicity, Consistency, Isolation, Durability OLTP → Online Transaction Processing OLAP → Online Analytical Processing TCP → Transmission Control Protocol IP → Internet Protocol DNS → Domain Name System MVC → Model View Controller MVVM → Model View ViewModel SDLC → Software Development Life Cycle CI/CD → Continuous Integration / Continuous Deployment JWT → JSON Web Token ORM → Object Relational Mapping API → Application Programming Interface REST → Representational State Transfer SOAP → Simple Object Access Protocol Big O → Time & Space Complexity Notation FIFO → First In First Out LIFO → Last In First Out 💬 Double Tap ❤️ for more! ----- 1.2 ₽ · /balance_help

𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗲𝘀𝗲 𝗛𝗶𝗴𝗵-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 🔥 This guide highlights 3 powerful skills that are opening doors to high-paying roles across tech and business .🎓 Perfect For 👨‍🎓 Students 💼 Freshers 📈 Job seekers trying to improve employability 🚀 Anyone who wants to build a future-proof career with better salary potential 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4vXeGmm 🚀 Start learning today. Build in-demand skills. Position yourself for better opportunities and bigger career growth.

🌐 Web Development Tools & Their Use Cases 💻✨ 🔹 HTML ➜ Building page structure and semantics 🔹 CSS ➜ Styling layouts, colors, and responsiveness 🔹 JavaScript ➜ Adding interactivity and dynamic content 🔹 React ➜ Creating reusable UI components for SPAs 🔹 Vue.js ➜ Developing progressive web apps quickly 🔹 Angular ➜ Building complex enterprise-level applications 🔹 Node.js ➜ Running JavaScript on the server side 🔹 Express.js ➜ Creating lightweight web servers and APIs 🔹 Webpack ➜ Bundling, minifying, and optimizing code 🔹 Git ➜ Managing code versions and team collaboration 🔹 Docker ➜ Containerizing apps for consistent deployment 🔹 MongoDB ➜ Storing flexible NoSQL data for apps 🔹 PostgreSQL ➜ Handling relational data and queries 🔹 AWS ➜ Hosting, scaling, and managing cloud resources 🔹 Figma ➜ Designing and prototyping UI/UX interfaces 💬 Tap ❤️ if this helped!

GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace

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🚀 Complete MERN Stack Roadmap 👨‍💻🔥 The MERN Stack is one of the most popular technologies for building modern web applications. 🌐 MERN stands for: ✔ M → MongoDB ✔ E → Express.js ✔ R → React.js ✔ N → Node.js If your goal is to become a Full Stack Web Developer, this roadmap can guide you from beginner to advanced level. 💯 🧠 STEP 1: Learn Web Development Basics Before learning MERN, understand how websites work. 📚 Learn: ✔ How the Internet Works ✔ HTTP & HTTPS ✔ Frontend vs Backend ✔ Browser & Servers ✔ APIs Basics 🌐 STEP 2: Master HTML, CSS & JavaScript These are the foundation of web development. 🏗 HTML Used to create website structure. Learn: ✔ Headings ✔ Forms ✔ Tables ✔ Semantic Tags ✔ Audio & Video 🎨 CSS Used for styling websites. Learn: ✔ Colors & Fonts ✔ Flexbox ✔ Grid ✔ Responsive Design ✔ Animations ⚡ JavaScript Makes websites interactive. Learn: ✔ Variables ✔ Functions ✔ Arrays & Objects ✔ Loops & Conditions ✔ DOM Manipulation ✔ ES6 Concepts ✔ Async/Await ✔ Fetch API 🛠 STEP 3: Learn Git & GitHub Version control is very important for developers. Learn: ✔ Git Basics ✔ Push & Pull ✔ Branching ✔ Merge ✔ Open Source Contribution ⚛ STEP 4: Learn React.js React is the frontend library used in MERN. 📚 Core Concepts: ✔ Components ✔ Props ✔ State ✔ Events ✔ Conditional Rendering ✔ Lists & Keys ✔ Forms Handling ⚡ Advanced React: ✔ Hooks ✔ useEffect ✔ useContext ✔ React Router ✔ API Integration ✔ Redux Toolkit ✔ Performance Optimization 🎨 STEP 5: Learn Tailwind CSS Modern frontend styling framework. Learn: ✔ Utility Classes ✔ Responsive Design ✔ Flex/Grid ✔ Dark Mode ✔ Components Styling 🟢 STEP 6: Learn Node.js Node.js allows JavaScript to run on servers. Learn: ✔ Modules ✔ File System ✔ Event Loop ✔ NPM ✔ Package Management ✔ Environment Variables 🚀 STEP 7: Learn Express.js Express helps build backend APIs easily. Learn: ✔ Routes ✔ Middleware ✔ REST APIs ✔ Request & Response ✔ Error Handling ✔ Authentication 🍃 STEP 8: Learn MongoDB MongoDB is a NoSQL database. Learn: ✔ Collections & Documents ✔ CRUD Operations ✔ Schema Design ✔ Mongoose ✔ Relationships ✔ Aggregation 🔐 STEP 9: Authentication & Security Very important for real-world projects. Learn: ✔ JWT Authentication ✔ Cookies & Sessions ✔ Password Hashing ✔ Role-Based Access ✔ API Security ☁️ STEP 10: Deployment Learn how to make your app live. Platforms: ✔ Vercel ✔ Render ✔ Netlify 🛠 Important Tools to Learn ✔ VS Code ✔ Postman ✔ GitHub ✔ MongoDB Compass ✔ Chrome DevTools 🔥 Best Projects for MERN Stack 🟢 Beginner Projects ✔ Todo App ✔ Weather App ✔ Notes App ✔ Calculator ✔ Quiz App 🟡 Intermediate Projects ✔ Blog Website ✔ Expense Tracker ✔ Chat Application ✔ Movie App ✔ Portfolio Website 🔴 Advanced Projects ✔ E-commerce Website ✔ Social Media App ✔ AI Chatbot ✔ Video Streaming Platform ✔ Learning Management System 📚 Best Resources to Learn MERN 🎥 YouTube Channels ✔ CodeWithHarry ✔ freeCodeCamp ✔ Traversy Media 🚀 Suggested Learning Order 1️⃣ HTML 2️⃣ CSS 3️⃣ JavaScript 4️⃣ Git & GitHub 5️⃣ React.js 6️⃣ Tailwind CSS 7️⃣ Node.js 8️⃣ Express.js 9️⃣ MongoDB 🔟 Deployment 💡 Advice for Beginners ❌ Don’t just watch tutorials ✅ Build projects alongside learning ❌ Don’t memorize code ✅ Understand logic and flow ❌ Don’t skip JavaScript basics ✅ Strong JavaScript = Strong MERN Developer 🔥 Mernstack Resources: https://whatsapp.com/channel/0029Vaxox5i5fM5givkwsH0A 💬 Tap ❤️ if this helped you!

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4 Career Paths In Data Analytics 1) Data Analyst: Role: Data Analysts interpret data and provide actionable insights through reports and visualizations. They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions. Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics. Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders. 2)Data Scientist: Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data. They develop models to predict future trends and solve intricate problems. Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization. Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies. 3)Business Intelligence (BI) Analyst: Role: BI Analysts focus on leveraging data to help businesses make strategic decisions. They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations. Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy. Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning. 4)Data Engineer: Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis. Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes. Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊

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Java vs Python Programming: Quick Comparison ✍ 📌 Java Programming • Strongly typed language • Object-oriented • Compiled, runs on JVM Best fields: • Backend development • Enterprise systems • Android development • Large-scale applications Job titles: • Java Developer • Backend Engineer • Software Engineer • Android Developer Hiring reality: • Popular in MNCs and legacy systems • Used in banking and enterprise apps India salary range: • Fresher: 4–7 LPA • Mid-level: 8–18 LPA Real tasks: • Build REST APIs • Backend services • Android apps • Large transaction systems 📌 Python Programming • Dynamically typed • Simple syntax • Interpreted language Best fields: • Data Analytics • Data Science • Machine Learning • Automation • Backend development Job titles: • Python Developer • Data Analyst • Data Scientist • ML Engineer Hiring reality: • High demand in startups and AI teams • Preferred for rapid development India salary range: • Fresher: 6–10 LPA • Mid-level: 12–25 LPA Real tasks: • Data analysis scripts • ML models • Automation tools • APIs with Django or FastAPI ⚔️ Quick comparison • Data handling: Java focuses on structured systems, Python handles data and files easily • Speed: Java runs faster in production, Python runs slower but builds faster • Learning: Java has steep learning curve, Python is beginner-friendly 🎯 Role-based choice • Backend Developer: Java for scalability, Python for quick APIs • Data Analyst: Python preferred, Java rarely used • Data Scientist: Python mandatory, Java optional • Android Developer: Java required, Python not used ✅ Best career move • Start with Python for quick entry • Add Java for strong backend roles • Pick based on your target job Which one do you prefer? Java 👍 Python ❤️ Both 🙏 None 😮

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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍

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