Getting Started with SQL: A Guide for Data Analysts

SQL (Structured Query Language) is often the first tool data analysts reach for — and for good reason. It’s powerful, widely used, and surprisingly readable once you get the hang of it.

In this post, we’ll walk through the basics — not to overwhelm, but to show how you can start using SQL to actually do analysis.

🧠 Why SQL Matters
Before Python notebooks, dashboards, or machine learning models — there’s SQL.

It helps answer questions like:

What products sold the most last month?

How many users signed up this week?

What’s the average salary in each department?

If you can ask it, SQL can probably answer it.

🛠️ The Building Blocks
Let’s walk through the essential commands.

SELECT — Choosing what you want to see

SELECT name, age
FROM employees;

WHERE — Filtering the results

SELECT *
FROM employees
WHERE age > 30;

Only returns employees older than 30.

ORDER BY — Sorting your output

SELECT name, hire_date
FROM employees
ORDER BY hire_date DESC;

Sorted from most recently hired to oldest.

➕ Arithmetic in SQL
SQL isn’t just about pulling data — you can also perform calculations right in your queries.

Basic math
SELECT price, quantity, price * quantity AS total_cost
FROM orders;

You can use +, -, *, and / just like in regular math.

Example with discounts
SELECT product_name, price, price - (price * 0.1) AS discounted_price
FROM products;

This calculates a 10% discount on each product.

💡 This post is about getting comfortable — opening the door to SQL without trying to sprint through it.

⏸️ Stopping Here (for Now)
No JOINs. No subqueries. We’ll save those for another day.

More posts coming soon, including how to group data, calculate summaries, and explore real datasets.

If you’re new to SQL (or remember how it felt to be), I’d love to hear from you — drop your thoughts or questions in the comments 💬