SQL for Data Reporting and Analysis

Introduction

• Presenter: Kevin
• Objective: To teach SQL for data reporting and analysis using free tools, assuming no prior knowledge.
• Goal: By the end, know how to retrieve data from databases and learn a trick for writing complex queries easily.

Why Learn SQL?

• All businesses use databases for data tracking.
• SQL unlocks hidden insights within data.
• Example: Using SQL at Microsoft to analyze user interactions on Office.com.

What is SQL?

• SQL: Structured Query Language, used to interact with databases.
• Functions: Retrieve, filter, sort, add, update, and remove data.
• Learning SQL: Accessible without a computer science degree.

Databases and RDBMS

• Database: A collection of related tables.
• RDBMS: Relational Database Management System (e.g., Oracle, PostgreSQL, Microsoft SQL Server, MySQL).
• Example: Kevin Cookie Company uses databases for tracking customers, orders, and products.

Setting Up the Tools

• Microsoft SQL Server: Chosen for its popularity and free tools.
• Editions: Developer (full functionality, not for business) vs. Express (limited functionality, usable for business).
• Installation: Basic installation recommended.

SQL Server Management Studio (SSMS)

• Purpose: A graphical tool to write queries.
• Connection: Connect to SQL Server using SSMS.
• Database Setup: Creating or restoring a database.

Database Structure

• Tables: Break data into related tables to avoid redundancy.
• Normalization: Process of minimizing data repetition.
• Primary Key: Unique identifier for records in a table.

Writing SQL Queries

• Select Statements: Retrieve specific data.
  • SELECT column_name FROM table_name
  • Use SELECT * to get all columns.
• Filtering Data: Use WHERE clause for conditions.
  • WHERE column_name = value for equality.
  • Logical operators like AND, OR, IN, NOT IN.

Advanced Query Topics

• Joins: Combine data from multiple tables.
  • Inner Join: Only overlapping data from both tables.
  • Left/Right Outer Join: Include all data from one table, matched with the other.
• Functions: Built-in SQL functions for data manipulation.
• Aggregations: Using functions like COUNT(), SUM(), GROUP BY.

Query Optimization and Tricks

• Aliases: Use table aliases to simplify queries.
• Query Designer: Visual tool in SSMS to construct queries easily.

Conclusion

• SQL's Value: Valuable skill to gain insights from data.
• Call to Action: Engage with comments for future SQL topics and subscribe for more content.