Prepared by the Uncodemy trainer team• Reviewed by Senior Data Analytics Lead• Last reviewed: 24 October 2026
Quick Answer
This page lists Uncodemy's SQL for Data Analysts PDFs, each as its own file. Analysts use SQL to pull data, calculate KPIs, segment customers and prepare data for dashboards. The core skills are SELECT with filters, GROUP BY, JOINs, CASE WHEN and, for deeper analysis, window functions.
Daily Analyst Metrics (Simulated)SQL Production KPIs
Repeat Customer Rate28.4% +3.2%
High-Value Segment$14,250 ↑ KPI
SELECT customer_id, COUNT(*) FROM orders GROUP BY customer_id HAVING COUNT(*) > 1;
What analysts actually do with SQL
An analyst rarely builds databases. The daily work is asking questions of existing tables: how much did we sell, which customers came back, which segment grew. That means filtering, grouping, joining tables and shaping results for a dashboard. Writing clear, correct queries and checking their row counts matters more than clever tricks.
Common analyst tasks and the SQL behind them
Everyday business analytical questions and their corresponding SQL implementation concepts:
Task
SQL idea
Total and average by group
GROUP BY with SUM, AVG, COUNT
Combine customers and orders
JOIN on the primary / foreign key
Customers with no orders
LEFT JOIN with IS NULL
Repeat customers
GROUP BY with HAVING COUNT(*) > 1
Segment customers
CASE WHEN on totals
Share of total
Window function SUM() OVER ()
Two examples (tested on a small practice dataset)
Practice dataset tested query patterns for finding repeat buyers and categorizing revenue tiers:
-- customers with more than one orderSELECT customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(*) > 1;
-- spend and segment per customerSELECT customer_id,
SUM(amount) AS total_spend,
CASE WHENSUM(amount) >= 300 THEN'High'ELSE'Standard'ENDAS segment
FROM orders
GROUP BY customer_id;
Common mistakes
Joining without checking row counts: a 1-to-many join can silently duplicate transactions and inflate revenue sums.
Averaging averages: mathematically flawed; always compute averages from raw atomic rows.
Ignoring NULLs: NULLs alter COUNT(column) vs COUNT(*) and distort AVG() results.
Not validating: compare one query result with a number you can verify by hand or from the source database.
Analyst validation rules
Always check COUNT(*) before and after applying joins.
Use COALESCE to cleanly handle NULL values in financial sums.
Confirm that distinct customer IDs match your active user counts.
Test edge cases such as customers with 0 orders or negative refunds.
SELECT with filters, GROUP BY and HAVING, JOINs, CASE WHEN, date functions and window functions cover most analyst work.
Group orders by customer and keep groups with more than one order using GROUP BY customer_id HAVING COUNT(*) > 1.
WHERE filters rows before grouping; HAVING filters groups after aggregation.
Entry-level roles need solid basics and joins. Window functions and CTEs are often asked in interviews and help with deeper analysis.
Check row counts at each step, compare totals with the source or a manual calculation, and test edge cases such as NULL values.
Yes! All SQL for Data Analysts PDFs provided on this Uncodemy page are 100% free for students, working professionals, and business analysts for learning and revision.
About these PDFs:
These PDFs are study material for learning and revision. They are not a certificate, a degree or a promise of a job, and technology changes, so check official documentation for the latest details. Spot an error? Write to info@uncodemy.com and the trainer team will review it.
