🗃️ SQL Interview Preparation

Your ultimate guide to mastering SQL for AI/ML interviews and portfolio-worthy projects

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📖 Introduction

Welcome to my SQL-Roadmap, your one-stop shop for mastering SQL in AI/ML interviews and beyond! 🚀 This repository is packed with hands-on queries, advanced techniques, real-world case studies, and epic projects to help you shine in technical interviews and data-driven ML roles. From core fundamentals to massive portfolio-building challenges, it’s designed to boost your confidence and land you that dream job with clarity and swagger. Let’s query our way to greatness!

🌟 What’s Inside?

• Queries Mastery: Nail SELECT, JOINs, aggregations, and advanced functions for coding tests.
• Database Concepts: Master indexing, normalization, schemas, and performance tuning.
• Hands-on Projects: Build end-to-end ML pipelines with Giant Projects to showcase on irohanportfolio.netlify.app.
• Real-World Case Studies: Solve industry scenarios to prep for FAANG-style interviews.
• Interview Question Bank: Tackle common and advanced SQL questions with clear answers.
• Performance Optimization: Learn pro tips for efficient, scalable SQL.

🔍 Who Is This For?

• Data Scientists leveling up for ML interviews.
• Machine Learning Engineers building SQL fluency for data pipelines.
• AI Researchers streamlining data preprocessing and analysis.
• Software Engineers transitioning to AI/ML roles.
• Freshers & Pros mastering SQL for data-driven careers.

🗺️ Comprehensive Learning Roadmap

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🗃️ SQL Fundamentals

📋 Data Query Language (DQL)

• SELECT Operations
  • SELECT Statement
  • DISTINCT
  • WHERE Clause
  • LIKE Operator
  • Wildcard Operators
  • IN Operator
  • BETWEEN Operator
  • IS NULL
  • AND, OR, NOT
• Sorting and Limits
  • ORDER BY
  • LIMIT
  • OFFSET
  • TOP
• Subqueries
  • Single-Row Subqueries
  • Multi-Row Subqueries
  • Correlated Subqueries
  • Nested Subqueries
• Conditional Logic
  • CASE Statements
  • COALESCE
  • NULLIF

✏️ Data Manipulation Language (DML)

• INSERT
  • Single Row Insert
  • Multiple Row Insert
  • Insert from SELECT
• UPDATE
  • Single Column Update
  • Multiple Column Update
  • Update with Conditions
• DELETE
  • Delete with Conditions
  • Delete All Rows

🛠️ Data Definition Language (DDL)

• Tables
  • CREATE TABLE
  • ALTER TABLE
  • DROP TABLE
  • TRUNCATE TABLE
• Constraints
  • Primary Key
  • Foreign Key
  • Unique
  • NOT NULL
  • CHECK
  • DEFAULT
• Views
  • CREATE VIEW
  • ALTER VIEW
  • DROP VIEW

🔐 Data Control Language (DCL)

• GRANT
  • Grant Permissions
  • Grant Roles
• REVOKE
  • Revoke Permissions
  • Revoke Roles

🔄 Transaction Control Language (TCL)

• COMMIT
• ROLLBACK
• SAVEPOINT
• SET TRANSACTION

🔗 Joins and Aggregations

• Joins
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL OUTER JOIN
  • Self-Join
  • Cross Join
• Aggregations
  • COUNT
  • SUM
  • AVG
  • MAX
  • MIN
  • GROUP BY
  • HAVING Clause
• Set Operations
  • UNION
  • UNION ALL
  • INTERSECT
  • EXCEPT

📝 Stored Procedures

• Creating Procedures
• Parameters
• Calling Procedures
• Dropping Procedures

⚡ Triggers

• Creating Triggers
• BEFORE Triggers
• AFTER Triggers
• Dropping Triggers

👈 Cursors

• Declaring Cursors
• Opening Cursors
• Fetching Data
• Closing Cursors

📊 Indexing

• Creating Indexes
• B-Tree Indexes
• Hash Indexes
• Specialty Indexes

🚀 Extra Modules

• Pivot Queries
• Dynamic Queries
• User-Defined Functions
• Window Functions
• Common Table Expressions
• Specialized Queries
• DateTime Functions

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🌌 Giant Projects

• Build massive SQL projects combining DQL, DML, DateTime Functions, and more for portfolio-ready ML solutions.
• Sub-Folders:
  • Project 1: Retail Sales Pipeline
  • Project 2: Time-Series Model Tracker
  • Project 3: Model Schema Builder
  • Project 4: Leaderboard Reporter
  • Project 5: Dynamic Model Evaluator

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📈 SQL Real World Case Studies

• Solve industry-inspired problems to prep for FAANG interviews, from data pipelines to analytics dashboards.
• Sub-Folders:
  • Interview Preparation For Real World Case Studies

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💡 Why Master SQL for AI/ML?

SQL is the backbone of AI/ML workflows, and here’s why it’s a game-changer:

1. Data Powerhouse: Drives preprocessing, feature engineering, and model evaluation for ML pipelines.
2. Interview Must-Have: 30% of AI/ML interviews test SQL for real-world scenarios—ace them with Case Studies.
3. Portfolio Edge: Build standout projects with Giant Projects to impress recruiters on irohanportfolio.netlify.app.
4. Scalability: Optimize queries for massive datasets, a top skill for 6 LPA+ roles.
5. Real-World Impact: From e-commerce analytics to model monitoring, SQL delivers insights that matter.

This repo is my roadmap to conquering SQL for technical interviews and AI/ML careers—join me to code, build, and win! 🌟

📆 Study Plan

• Week 1-2: DQL and Basic Queries
• Week 3-4: DML and DDL Mastery
• Week 5-6: Joins, Aggregations, and TCL
• Week 7-8: DCL, Stored Procedures, and Triggers
• Week 9-10: Cursors and Indexing Optimization
• Week 11-12: Extra Modules (Window Functions, DateTime Functions)
• Week 13-14: Tackle Giant Projects for portfolio-building
• Week 15-16: Solve SQL Real World Case Studies for interview prep

🤝 Contributions

Love to collaborate? Here’s how! 🌟

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/amazing-addition).
3. Commit your changes (git commit -m 'Add some amazing content').
4. Push to the branch (git push origin feature/amazing-addition).
5. Open a Pull Request.

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Happy Learning and Good Luck with Your Interviews! ✨