🗄️ Phase 3 | SQL & Databases

Welcome to Phase 3 of your data journey! In this module, you’ll learn Structured Query Language (SQL) — a must-have skill for querying, analyzing, and managing relational databases.

Whether you’re extracting customer behavior data or building automated reports, SQL is the language that brings your data to life.

---

1️⃣ SQL Basics

Start with the essentials of querying a relational database.

• 🏗️ What is SQL? Introduction to relational databases & SQL structure
• 📥 SELECT Statements – Extract columns from tables
• 🧮 Filtering – Use WHERE clauses for targeted results
• 📊 Aggregation – GROUP BY and HAVING to summarize data
• 🔢 Sorting & Limiting – ORDER BY and LIMIT to control output
• 🧠 Conditional Logic – Use CASE statements for if/then logic

💡 Think of SQL as asking structured questions to your database and getting specific answers.

---

2️⃣ Working with Multiple Tables

Real-world data is spread across multiple tables. Here’s how to combine it effectively:

• 🔗 Relationships: Understand One-to-One, One-to-Many, and Many-to-Many relationships
• 🤝 JOINs: Master INNER, LEFT, RIGHT, and FULL OUTER JOINs
• 📦 Subqueries: Write nested queries for more complex logic
• 🧩 Set Operations: Use UNION, INTERSECT, and EXCEPT to combine query results

---

3️⃣ Data Aggregation & Filtering

Learn to summarize and filter data effectively for insights:

• ➕ Aggregation functions: SUM(), AVG(), COUNT(), MIN(), MAX()
• 🎯 Filter after grouping with HAVING vs. before grouping with WHERE
• 🧠 Conditional Aggregation: Apply logic inside aggregation (e.g., COUNT(CASE WHEN...))

🧩 These techniques are the building blocks of dashboards, reports, and deep dives.

---

4️⃣ Advanced SQL & Data Transformation

Push your SQL skills further with these powerful tools:

🪟 Window Functions

• Rank, sort, and perform calculations across partitions of your data using:
  • ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
  • LEAD() and LAG() for comparing rows

🧱 Common Table Expressions (CTEs)

• Write cleaner queries with reusable logic using WITH clauses
• Create recursive CTEs for hierarchical data

🧼 Data Cleaning with SQL

• Handle:
  • Missing values – COALESCE()
  • Duplicate entries – ROW_NUMBER() filtering
  • Text cleanup – TRIM(), REPLACE(), LOWER(), etc.

---

🚀 Query Optimization & Performance

Make your SQL run faster and scale better:

• ⚡ Indexing for quicker searches
• 🛑 Avoid SELECT * — specify only what you need
• 🧩 Use appropriate data types and constraints
• 🔍 Learn how to read execution plans and optimize queries

---

🎯 What’s Next?

By completing this phase, you’ll be comfortable querying structured data, building complex logic, and optimizing performance — essential for roles in data analysis and BI.

In Phase 4, we’ll dive into Python for Data Analysis and bring automation and flexibility to your data workflows.

Structured data, structured thinking. Let SQL be your superpower in data analysis.

---