Overview
SQL Business Logic &
Data Handling Practice
Working with business-ready data — improving readability, handling missing values, applying conditional logic, and summarizing performance metrics.
Course Learning Objectives
- Use SQL Aliases (AS) to rename columns in query output for professional, business-friendly reports.
- Write SQL Comments using — and /* */ to document query logic and support team collaboration.
- Identify and filter missing data using IS NULL and IS NOT NULL for data quality and operational analysis.
- Apply conditional business logic using CASE to categorise records, create dynamic labels, and embed business rules in SQL output.
- Replace missing values with meaningful defaults using IFNULL and COALESCE for cleaner, standardised reports.
- Summarise business performance using Aggregate Functions — COUNT, SUM, AVG, MIN, and MAX.
- Use AI tools to generate, verify, and troubleshoot business logic and data handling SQL queries.
Part 1 covers SQL aliases — the AS keyword that renames columns in query output without changing anything in the database. Students learn to create single-word and multi-word aliases using backticks, and combine aliases with ROUND() for professional report-ready output.
Using AS to replace raw database column names with clear business labels in output.
How aliases make query results immediately understandable for business stakeholders.
Creating report-ready column headers for dashboards and shared outputs.
Combining aliases with ROUND() and backticks for polished financial and operational reports.
- Practice focus: renaming columns across users, transactions, products, orders, subscriptions, support tickets, campaigns, and sessions
- 8 practice queries — each with an AI prompt and a validated SQL answer
Part 2 covers SQL comments — the — and /* */ syntax for annotating query logic. Students learn to write single-line comments for brief annotations and multi-line comments for business context documentation. Comments are invisible to the database and have no effect on query results.
Using — to annotate individual lines and explain specific conditions or calculations.
Using /* */ to document the business purpose and context of larger query blocks.
Standard professional practices for making SQL queries readable and maintainable.
Writing comments that explain business rules and filtering decisions to collaborators.
- Practice focus: adding comments to queries across users, transactions, products, orders, subscriptions, support tickets, campaigns, and sessions
- 8 practice queries — each with an AI prompt and a validated SQL answer
Part 3 covers NULL handling — identifying and filtering rows with missing data. Students learn why NULL cannot be compared with = and why IS NULL and IS NOT NULL are the correct operators. They practise NULL detection on subscription end dates and support ticket resolution dates, and combine NULL filters with AND and ORDER BY.
What NULL means — missing, unknown, or unavailable — and how it differs from zero or empty text.
Filtering rows where a column has no recorded value using the correct SQL operator.
Filtering rows where a column contains an actual value — the complement of IS NULL.
Combining IS NULL and IS NOT NULL with AND and ORDER BY for operational analysis.
- Practice focus: detecting missing end dates and resolution dates across subscriptions and support tickets
- 8 practice queries — each with an AI prompt and a validated SQL answer
Part 4 covers the CASE statement — the SQL tool for embedding conditional business logic directly in query output. Students learn to write CASE…WHEN…THEN…ELSE…END statements, build multi-condition classification rules, and create dynamic label columns for segmentation and reporting.
How CASE evaluates each row against conditions and returns different output for each.
Using CASE to classify users, transactions, and subscriptions into business segments.
Creating readable, report-ready classification columns without modifying the database.
Embedding pricing tiers, value thresholds, and operational rules directly in SQL output.
- Practice focus: applying conditional logic across transactions, users, products, orders, subscriptions, support tickets, sessions, and campaigns
- 8 practice queries — each with an AI prompt and a validated SQL answer
Part 5 covers IFNULL and COALESCE — the MySQL functions for replacing NULL values with meaningful defaults in query output. Students learn the difference between the two functions, when to use each, and how to combine them with ORDER BY and additional columns for complete, clean business reports.
How IFNULL and COALESCE substitute readable labels wherever NULL would otherwise appear.
Practical application to subscription end dates and support ticket resolution dates.
Using replacement text like “Active Subscription” and “Not Resolved” for report clarity.
Combining NULL functions with ORDER BY for sorted, standardised operational reports.
- Practice focus: replacing NULL end dates and resolution dates in subscriptions and support tickets with business-friendly labels
- 8 practice queries — each with an AI prompt and a validated SQL answer
Part 6 covers aggregate functions — the five core SQL functions for summarising business data. Students learn to count records, calculate total revenue and subscription fees, measure average prices and transaction amounts, and identify minimum and maximum values, always using ROUND() for clean financial output.
How aggregate functions collapse many rows into a single calculated business metric.
Counting all rows with COUNT(*) or non-NULL values in a specific column.
Calculating total revenue and average values — always paired with ROUND() for financial output.
Identifying the smallest and largest values — pricing extremes, date ranges, revenue boundaries.
- Practice focus: producing business summary metrics across users, transactions, products, subscriptions, and support tickets
- 8 practice queries — each with an AI prompt and a validated SQL answer
Course at a Glance
Six parts · 48 practice queries · same tables as Parts 1 & 2
| Part | Topic | Core Focus | Practice |
|---|---|---|---|
| Part 1 | SQL Aliases | AS keyword, column renaming, backticks for multi-word labels, report formatting | 8 Queries |
| Part 2 | SQL Comments | — single-line, /* */ multi-line, query documentation, business rule annotation | 8 Queries |
| Part 3 | NULL Values | IS NULL, IS NOT NULL, missing data detection, combining with AND and ORDER BY | 8 Queries |
| Part 4 | CASE | WHEN…THEN…ELSE…END, conditional logic, dynamic labels, business classification | 8 Queries |
| Part 5 | IFNULL / COALESCE | NULL replacement, default values, standardising incomplete business data in output | 8 Queries |
| Part 6 | Aggregate Functions | COUNT(), SUM(), AVG(), MIN(), MAX() — business performance summaries with ROUND() | 8 Queries |
Every practice question in this course includes a business question, an AI prompt for ChatGPT or Copilot, the validated SQL answer, and guidance on interpreting the result. Students learn to prompt effectively, verify business logic, troubleshoot conditional and aggregation queries, and connect output to real analytical decisions.
What You Leave With
Six business logic and data handling commands. Forty-eight practice queries. Report-ready SQL skills.
Professional, business-friendly column labels for any report or dashboard output.
The habit and skill of documenting SQL logic for maintenance and collaboration.
Correct identification of missing data using IS NULL and IS NOT NULL.
Conditional business classification directly in SQL — segmentation without external tools.
Clean, standardised reports with meaningful defaults replacing empty NULL cells.
COUNT, SUM, AVG, MIN, MAX — the metrics that power KPIs and executive reporting.
The ability to prompt, verify, and troubleshoot business logic SQL in a professional workflow.
Certificate of Completion from the Canadian Institute of Artificial Intelligence (CiAI), powered by DataSoSi.
