Module 1 — Introduction to SQL & Oracle

Learning Goals

• Understand what SQL is and why it matters.
• Explore Oracle Database architecture.
• Learn how SQL, PL/SQL, and database objects fit together.

Topics

• What SQL Does: data retrieval, manipulation, definition, control
• Oracle Database Components: tablespaces, schemas, users
• Tools for Writing SQL: SQL*Plus, SQL Developer, SQL

Module 2 — SQL and Relational Database Concepts

Learning Goals

• Understand relational theory and table relationships.
• Learn keys, constraints, and normalization.

Topics

• Tables, Rows, Columns
• Primary & Foreign Keys
• Normalization Basics

Module 3 — SELECT Statements (Core Querying)

Learning Goals

• Master the basic SELECT syntax.
• Retrieve and format data.

Topics

• SELECT Syntax Structure
• Column Aliases
• DISTINCT Keyword

Module 4 — Filtering Data with WHERE Clause Operators

Learning Goals

• Apply conditions to filter rows.

Topics

• Comparison Operators: =, <, >, <>
• Logical Operators: AND, OR, NOT
• Pattern Matching: LIKE, ESCAPE
• Range & Set Tests: BETWEEN, IN, IS NULL

Module 5 — Sorting Data with ORDER BY

Learning Goals

• Sort results in ascending/descending order.
• Sort by expressions and multiple columns.

Topics

• ORDER BY Basics
• Sorting by Aliases
• NULLS FIRST / NULLS LAST

Module 6 — SQL CASE Logic

Learning Goals

• Use conditional logic inside queries.

Topics

• Simple CASE
• Searched CASE
• Derived Columns with CASE

Module 7 — SQL Built‑In Functions

Learning Goals

• Use Oracle’s built‑in functions for data transformation.

Topics

• String Functions: SUBSTR, INSTR, REPLACE
• Numeric Functions: ROUND, TRUNC, MOD
• Date Functions: SYSDATE, ADD_MONTHS, MONTHS_BETWEEN
• Conversion Functions: TO_CHAR, TO_DATE, TO_NUMBER

Module 8 — Aggregate Functions, GROUP BY, HAVING

Learning Goals

• Summarize data using aggregates.
• Group and filter aggregated results.

Topics

• Aggregate Functions: SUM, AVG, COUNT, MIN, MAX
• GROUP BY Rules
• HAVING Clause

Module 9 — DML: Insert, Update, Delete, Merge, Truncate

Learning Goals

• Modify data safely and efficiently.

Topics

• INSERT Variants
• UPDATE with Conditions
• DELETE vs TRUNCATE
• MERGE for Upserts

Module 10 — Inner & Outer Joins

Learning Goals

• Combine data from multiple tables.

Topics

• INNER JOIN
• LEFT/RIGHT OUTER JOIN
• FULL OUTER JOIN
• ANSI vs Oracle Join Syntax

Module 11 — Basic Subqueries

Learning Goals

• Use subqueries to solve multi‑step problems.

Topics

• Single‑Row Subqueries
• Multi‑Row Subqueries
• Subqueries with IN, ANY, ALL

Module 12 — Join Review + Subquery Table Expressions

Learning Goals

• Reinforce join logic.
• Use subqueries as tables.

Topics

• Inline Views
• Common Table Expressions (WITH)
• Nested Subqueries

Module 13 — Set Operations

Learning Goals

• Combine result sets.

Topics

• UNION vs UNION ALL
• INTERSECT
• MINUS

Module 14 — Advanced Grouping: CUBE, ROLLUP, GROUPING SETS

Learning Goals

• Build multi‑dimensional summaries.

Topics

• ROLLUP for Hierarchical Totals
• CUBE for All Combinations
• GROUPING SETS for Custom Aggregations
• GROUPING() Function

Module 15 — Solving Complex SQL Problems

Learning Goals

• Apply all learned techniques to real‑world scenarios.

Topics

• Top‑N Queries
• Analytic Thinking with SQL
• Multi‑Step Transformations

Module 16 — PIVOT and UNPIVOT

Learning Goals

• Transform rows to columns and vice versa.

Topics

• PIVOT Syntax
• UNPIVOT Syntax
• Dynamic Pivoting Concepts

Module 17 — Temporary Tables & Views

Learning Goals

• Use database objects to simplify complex logic.

Topics

• Global Temporary Tables
• Views for Abstraction
• Materialized Views Overview

Module 18 — Window and Analytical Functions

Learning Goals

Understand the purpose of window functions and how they differ from traditional aggregate queries with clause partitions, ranking, value and statistical  , analytical functions  and Perform running totals, moving averages, and period‑over‑period comparisons.

Topics

• Transform business questions into analytic SQL solutions using window functions.
• Identify when analytic functions are more efficient or expressive than subqueries or joins.
• Build multi‑step analytical queries that combine ranking, aggregates, and value comparisons.
• Interpret analytic function output to support reporting, forecasting, and trend analysis.