15 SQL Commands Every Data Analyst Must Learn

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If you want to work with data professionally, SQL is non-negotiable.

Dashboards, BI tools, AI models, and reports all depend on structured data, and SQL is the language that lets you ask the right questions from that data.

As a data analyst, you don’t need all of SQL — but you must master the essentials.
Below are 15 SQL commands that cover 90% of real-world analyst work.

1. SELECT — Fetching Data

The foundation of SQL. Every analysis starts here.

SELECT * FROM sales;

Used to retrieve columns and rows from a table.

2. WHERE — Filtering Records

Filters data based on conditions.

SELECT *
FROM sales
WHERE region = 'North';

Critical for isolating relevant data.

3. DISTINCT — Removing Duplicates

Helps identify unique values.

SELECT DISTINCT country
FROM customers;

Very common in profiling data.

4. ORDER BY — Sorting Results

Sorts data in ascending or descending order.

SELECT *
FROM sales
ORDER BY sales_amount DESC;

Useful for ranking analysis.

5. LIMIT / TOP — Controlling Output Size

Limits the number of rows returned.

SELECT *
FROM sales
ORDER BY sales_amount DESC
LIMIT 10;

Perfect for “Top 10” style insights.

6. COUNT() — Counting Rows

Returns the number of records.

SELECT COUNT(*) FROM orders;

Used in almost every KPI calculation.

7. SUM() — Adding Values

Aggregates numeric columns.

SELECT SUM(revenue)
FROM sales;

Essential for financial metrics.

8. AVG() — Calculating Averages

Finds mean values.

SELECT AVG(order_value)
FROM orders;

Used in performance and trend analysis.

9. MIN() & MAX() — Finding Extremes

Identifies smallest and largest values.

SELECT MIN(sales_date), MAX(sales_date)
FROM sales;

Helpful for date range checks.

10. GROUP BY — Aggregation by Category

Groups rows for summary analysis.

SELECT region, SUM(revenue)
FROM sales
GROUP BY region;

The backbone of analytical queries.

11. HAVING — Filtering Aggregated Data

Filters results after aggregation.

SELECT region, SUM(revenue)
FROM sales
GROUP BY region
HAVING SUM(revenue) > 100000;

A common interview favorite.

12. INNER JOIN — Combining Related Tables

Returns matching records from both tables.

SELECT *
FROM orders o
INNER JOIN customers c
ON o.customer_id = c.customer_id;

Used constantly in real datasets.

13. LEFT JOIN — Keeping All Primary Data

Keeps all records from the left table.

SELECT *
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id;

Perfect for detecting missing data.

14. CASE — Conditional Logic

Adds logic similar to IF-ELSE.

SELECT
  sales_amount,
  CASE
    WHEN sales_amount > 50000 THEN 'High'
    ELSE 'Low'
  END AS sales_category
FROM sales;

Extremely valuable for business rules.

15. NULL Handling (IS NULL / COALESCE)

Deals with missing values.

SELECT COALESCE(discount, 0)
FROM sales;

Avoids incorrect calculations and blanks.

Why These 15 Commands Matter

With just these commands, you can:

• Analyze trends
• Prepare data for Power BI or Tableau
• Validate dashboards
• Support AI and ML workflows
• Answer 90% of stakeholder questions

You don’t need to be a SQL expert — you need to be SQL effective.

Tools will change.
Dashboards will evolve.
AI will accelerate analysis.

But SQL remains the backbone of data work.

If you’re serious about being a data analyst, master these 15 commands — and everything else becomes easier.

 

PowerBI Course at Rs 99.

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