SQL for Data Analysis: A Complete Beginner's Guide

How to use this SQL guide

This guide is designed for a learner who has never queried a database. You do not need prior programming knowledge. Read Stage 1 before installing anything, complete the environment setup, and then type each query yourself. Reading SQL can make a command look familiar; running it, predicting the result, and correcting mistakes is what builds usable skill.

Every worked example uses the same NovaMart database so that concepts connect instead of appearing as isolated syntax. Five labels will help you navigate the learning process:

Stage 1: Database foundations

What is a database?

A database is an organized collection of data stored so it can be searched, filtered, validated, and connected efficiently. A spreadsheet is excellent for small tables, calculations, and direct visual editing. A database becomes more useful when data grows, many people or systems rely on it, or one business process spans several related tables.

Relational databases and DBMS software

A relational database separates information into related tables. Instead of repeating a customer's name, state, email, and phone number on every order, one customers table stores the customer record and an orders table refers to that customer through an ID. The software that stores and manages these tables is called a database management system, or DBMS.

This guide standardizes on PostgreSQL. Most core statements transfer directly to other systems, although commands such as limiting rows can vary.

Tables, rows, columns, and data types

A table is one organized set of related data. A row, also called a record, is one complete entry. A column, also called a field, is one defined attribute stored for every row. Columns have data types, such as integer, text, date, decimal, or boolean. Data types help the database reject invalid input and handle sorting or calculations correctly.

Primary keys, foreign keys, and relationships

A primary key uniquely identifies each row in its own table and cannot be NULL. A foreign key points to a primary key in another table and creates a relationship. A unique key also prevents duplicate values, but it does not necessarily serve as the table's main row identifier.

• One-to-one: one row in Table A matches one row in Table B.
• One-to-many: one customer can have many orders. This is the most common business relationship.
• Many-to-many: many students can take many courses, usually connected by an enrolment table.

What SQL is and how a query is structured

SQL stands for Structured Query Language. It is the language used to retrieve, filter, summarize, combine, and change relational data. Keywords have a defined order. Not every query uses every clause, but clauses that are present must appear in the right sequence.

SELECT column_name       -- what to retrieve
FROM table_name          -- which table
WHERE condition          -- which rows, optional
ORDER BY column_name;    -- how to sort, optional

SQL keywords are conventionally written in uppercase and table or column names in lowercase. PostgreSQL does not require that style, but consistency makes long queries easier to scan. Two dashes begin a line comment, and a semicolon marks the end of a statement.

Set up PostgreSQL and DBeaver

PostgreSQL is the database server that stores the data. DBeaver Community is the visible application used to connect to that server, browse tables, write queries, and inspect results. Install PostgreSQL first because DBeaver needs a database server to connect to.

Install PostgreSQL

1. Visit postgresql.org/download and choose your operating system.
2. Run the standard installer and keep the core components selected.
3. Create a strong password for the postgres superuser and store it securely.
4. Keep the default port 5432 unless another local service already uses it.
5. Complete the installation. Stack Builder is optional and is not required for this guide.

Install and connect DBeaver

1. Download DBeaver Community for Windows, macOS, or Linux.
2. Open DBeaver and create a new database connection.
3. Select PostgreSQL. Use host localhost, port 5432, username postgres, and your PostgreSQL password.
4. Choose Test Connection. Allow DBeaver to download the PostgreSQL driver if requested.
5. Finish the connection when the test reports success.

The Database Navigator displays connections, databases, schemas, and tables. The SQL Editor is where you write statements. A table's Data tab lets you inspect rows. In the editor, Ctrl+Enter on Windows or Cmd+Return on macOS runs the current statement.

Build the NovaMart practice database

Create a database named novamart, open a fresh editor connected specifically to it, and then create two related tables. PostgreSQL does not use MySQL's USE database command; the editor's active connection determines where the statement runs.

CREATE DATABASE novamart;

After creating the database, refresh the Navigator, right-click novamart, and open a new SQL Editor. Then create and populate the tables:

CREATE TABLE customers (
  customerid INTEGER PRIMARY KEY,
  customername TEXT,
  state TEXT,
  gender TEXT,
  email TEXT
);

CREATE TABLE orders (
  orderid INTEGER PRIMARY KEY,
  customerid INTEGER REFERENCES customers(customerid),
  orderdate DATE,
  product TEXT,
  amount NUMERIC(10,2),
  salesperson TEXT
);

INSERT INTO customers VALUES
  (1, 'Adaeze Obi', 'Rivers', 'Female', 'adaeze.obi@mail.com'),
  (2, 'Tunde Bakare', 'Lagos', 'Male', 'tunde.bakare@mail.com'),
  (3, 'Chinyere Eze', 'Abuja', 'Female', 'chinyere.eze@mail.com'),
  (4, 'Ibrahim Musa', 'Kano', 'Male', 'ibrahim.musa@mail.com'),
  (5, 'Ngozi Umeh', 'Rivers', 'Female', 'ngozi.umeh@mail.com');

INSERT INTO orders VALUES
  (101, 1, '2025-01-12', 'Laptop', 450000.00, 'Amaka'),
  (102, 2, '2025-01-18', 'Monitor', 125000.00, 'Bello'),
  (103, 1, '2025-02-03', 'Keyboard', 35000.00, 'Amaka'),
  (104, 3, '2025-02-16', 'Printer', 185000.00, 'Chidi'),
  (105, 5, '2025-03-08', 'Router', 68000.00, 'Bello');

Expand novamart → Schemas → public → Tables and inspect each table's Data tab. If both tables display their rows, the practice environment is ready.

Stage 2: Retrieve and filter data

SELECT specific columns

SELECT names the columns the database should return. An asterisk asks for every column, while an explicit list returns only what the analysis needs.

SELECT *
FROM customers;

SELECT customername, state
FROM customers;

Rename output with aliases

An alias changes a result heading without renaming the stored column. Use quotes when an output label contains spaces.

SELECT customername AS "Customer Name",
       state AS "Location"
FROM customers;

Remove duplicate results with DISTINCT

SELECT DISTINCT state
FROM customers;

NovaMart has five customer rows but only four distinct states. With several selected columns, DISTINCT evaluates the complete combination. Two rows collapse only when every selected value matches.

Filter rows with WHERE

SELECT orderid, product, amount
FROM orders
WHERE amount > 100000;

Text and date literals need single quotes. Numeric values do not. Comparison operators include =, <> or !=, >, <, >=, and <=.

Combine conditions with AND, OR, and NOT

SELECT *
FROM orders
WHERE salesperson = 'Bello'
  AND amount > 50000;

AND requires every condition to be true. OR accepts a row when at least one condition is true. NOT reverses a condition. Use parentheses when combining AND and OR so the intended logic is unambiguous.

Stage 3: Filter data properly

Match a list with IN

SELECT customername, state
FROM customers
WHERE state IN ('Rivers', 'Lagos', 'Abuja');

IN is clearer than repeating the same column through several OR conditions. Text values need quotes; numeric values do not.

Match an inclusive range with BETWEEN

SELECT orderid, product, amount
FROM orders
WHERE amount BETWEEN 50000 AND 150000;

BETWEEN includes both boundaries. The same operator works with suitable date columns:

SELECT *
FROM orders
WHERE orderdate BETWEEN '2025-01-01' AND '2025-03-31';

Search text patterns with LIKE

SELECT customername
FROM customers
WHERE customername LIKE 'A%';

Test missing values correctly

SELECT *
FROM customers
WHERE email IS NULL;

SELECT *
FROM customers
WHERE email IS NOT NULL;

Combine several filters safely

SELECT *
FROM orders
WHERE salesperson IN ('Amaka', 'Bello')
  AND amount > 50000
  AND orderdate BETWEEN '2025-01-01' AND '2025-03-31';

Parentheses matter when OR is added. In the following query, the state alternatives are evaluated together before the amount condition:

SELECT *
FROM customers
WHERE (state = 'Rivers' OR state = 'Lagos')
  AND customerid > 1;

Stage 4: Sort and limit results

Sort results with ORDER BY

SELECT product, amount
FROM orders
ORDER BY amount DESC;

Ascending order is the default. Add DESC for descending order. Multiple sort columns provide a predictable tiebreaker:

SELECT salesperson, product, amount
FROM orders
ORDER BY salesperson ASC, amount DESC;

Limit the number of rows

SELECT product, amount
FROM orders
ORDER BY amount DESC
LIMIT 3;

PostgreSQL, MySQL, and SQLite use LIMIT. SQL Server commonly uses TOP after SELECT. Pair the row cap with ORDER BY whenever the question asks for the highest, lowest, newest, oldest, best, or worst records.

Worked assignment answers

Attempt each task before opening its solution. The struggle to recall and assemble a query is part of learning; recognizing an answer after it is revealed is not the same as producing it independently.

SELECT * FROM books;

SELECT title AS "Book Name", genre
FROM books;

SELECT DISTINCT genre
FROM books;

SELECT * FROM bookings
WHERE roomtype IN ('Deluxe', 'Suite');

SELECT * FROM bookings
WHERE totalcost BETWEEN 50000 AND 200000;

SELECT * FROM bookings
WHERE guestname LIKE 'K%';

SELECT * FROM bookings
WHERE specialrequest IS NULL;
-- Use specialrequest = '' only when the source stores empty text.

SELECT * FROM leaderboard
ORDER BY score DESC
LIMIT 3;

SELECT * FROM leaderboard
ORDER BY score DESC, playername ASC;

SELECT * FROM leaderboard
ORDER BY score ASC
LIMIT 1;

SQL syntax cheat sheet

Frequently asked questions

Continue your data-analysis pathway

This guide covers the first four stages of the JENECONK SQL learning series. Continue building your wider analyst toolkit through data cleaning in Excel, Power BI for business reporting, and the data analyst career roadmap. For guided practice, projects, and facilitator support, explore the JENECONK AI Training Academy.

JENECONK's training team developed this resource as a practical foundation for learners who need to move from spreadsheet familiarity into structured data work. Examples are written for hands-on use, and the guide will be expanded as later stages in the SQL series are published.