SELECT: choosing columns

Lesson Description

Pick the columns you want from a table.

Easy Project

Mini project: Turn this lesson into a real-world query artifact by writing one clean business report query and validating output quality.

SELECT — pick your columns

Every SQL query starts with SELECT. It tells the database which columns to return.

Basic syntax

SELECT column1, column2
FROM table_name;

Selecting specific columns

Listing columns explicitly is best practice — it self-documents intent and is resilient to schema changes.

SELECT first_name, last_name, salary
FROM employees;

Wildcard: SELECT *

Returns every column. Useful for quick exploration, but avoid it in production — future columns appear automatically and can break downstream code.

SELECT * FROM employees;

Column aliases with AS

AS renames a column in the result set. The underlying table is unchanged.

SELECT
  first_name  AS given_name,
  salary      AS monthly_pay
FROM employees;

Computed expressions

Arithmetic and string operations work directly in SELECT:

SELECT
  first_name,
  salary * 12                    AS annual_salary,
  first_name || ' ' || last_name AS full_name
FROM employees;

Key rules

Columns are separated by commas.
FROM specifies the source table.
Aliases set in SELECT cannot be referenced in WHERE — they do not exist yet at that stage.
Without WHERE, every row is returned.

Learning context

This lesson is part of SQL Fundamentals. Focus on the core idea in SELECT: choosing columns, then validate with deliberate practice.

What to master

Understand clause order and intent
Write deterministic result queries
Use clear aliases and readable structure

Common mistakes

Skipping ORDER BY in final outputs
Using joins before validating base rows
Not checking row counts after changes

High-level strategy

Start simple, then layer complexity
Run often and compare expected shape
Refactor with CTEs when logic grows

Task ladder

Baseline query: get a correct first output.
Add one complication: edge case, join, filter, or ranking rule.
Re-run and refine until output is stable and explainable.

Transparent data checks

Check row count before and after each major clause.
Validate one manual sample row for correctness.
Confirm final ordering and column naming for trust.

Retention loop

Run the solution once.
Rewrite from memory with one variation.
Explain the logic in plain English in one sentence.

Logical reasoning for commands

WHERE

Why: Limits rows to only the business-relevant subset.

Memory cue: Filter early to reduce noise.

ORDER BY

Why: Makes output deterministic and reviewable.

Memory cue: No ORDER BY means no guaranteed row order.

AS alias

Why: Makes output columns readable for teams and reports.

Memory cue: If the name is clear, the query is easier to trust.

Concept check

Quick check: SELECT: choosing columns

3 quick questions. One at a time. Instant score at the end.

Exercise 1

List all first and last names

Enterprise Workforce Analyticseasy

Return the first_name and last_name of every employee.

Run a query to see results here.

Exercise 2

Rename the salary column

Enterprise Workforce Analyticseasy

Return every employee's first_name and their salary aliased as monthly_pay.

Run a query to see results here.

Exercise 3

Compute annual salary

Enterprise Workforce Analyticseasy

Return first_name, last_name, and salary * 12 aliased as annual_salary for every employee.

Run a query to see results here.

Exercise 4

Job titles and salary range

Enterprise Workforce Analyticseasy

Return title, min_salary, and max_salary from the jobs table, ordered by min_salary ascending.

Run a query to see results here.

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