Overview

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Learn SQL and Database Analysis From Scratch! Go from no database experience to writing advanced SQL queries and analyzing real-world data with PostgreSQL and Python. This course gives you the core knowledge and practical skills to understand, query, and analyze data in relational databases — with no prior background required. You'll start by learning why organizations use database systems, how relational and NoSQL databases differ, and how databases fit into a standard system architecture. From there, you'll learn to model real-world business rules using Entity-Relationship (ER) diagrams, then convert those models into concrete relational schemas with tables, primary keys, and foreign keys. The core of the course is SQL programming, taught using PostgreSQL. You'll write queries to retrieve, filter, join, sort, and aggregate data — then progress to advanced techniques including nested queries, Common Table Expressions (CTEs), Views, Window Functions, and User-Defined Functions. These skills let you extract complex business insights directly from a database. The course closes by bridging SQL with general-purpose programming: you'll compare SQL with Python's pandas library, learn when to use each, and apply DuckDB to run SQL queries directly on DataFrames and CSV files within a Python environment. A real-world ambulance allocation case study ties analysis and visualization together. Who this is for: Aspiring data analysts, business analysts, developers, and anyone who works with data and wants to build practical, job-ready database and SQL skills from the ground up.

Syllabus

The Database Landscape
- Discover why organizations rely on databases instead of traditional files like Excel. Explore how Database Management Systems solve problems like data redundancy, inconsistency, and concurrency. Compare Relational and NoSQL databases to understand when to use each. Examine system architecture through the Taiwan High Speed Rail (THSR) ticketing case study, and identify the professional roles that make up the database ecosystem.
Relational Data Model
- Design a relational database from the ground up. Model business rules using entities, attributes, and relationships in the Entity-Relationship (ER) framework. Construct ER diagrams to visualize requirements for a retail business case study. Then convert your ER models into concrete relational schemas—complete with tables, primary keys, and foreign keys—using a systematic 7-step mapping algorithm.
Basic SQL Programming
- Write SQL to manage and analyze data in PostgreSQL. Create a Data Dictionary to document table structures, data types, and constraints. Build and modify tables using Data Definition Language (DDL) with referential integrity constraints. Construct queries to retrieve, filter, join, sort, and aggregate data for business insights. Insert, update, and delete records while maintaining data integrity.
Advanced SQL Programming
- Master advanced SQL techniques for deep data analysis. Construct nested queries, then refactor them for readability using Common Table Expressions (CTEs) and Views. Handle null values, implement conditional logic with CASE, and perform conditional aggregation with FILTER. Calculate rankings, running totals, and moving averages using window functions. Encapsulate reusable logic in User-Defined Functions (UDFs).
Briding SQL with General Programming
- Connect SQL skills to general-purpose programming. Compare declarative (SQL) and imperative (Pandas) paradigms to understand when each excels. Query CSVs and DataFrames directly using DuckDB within Python. Apply your skills to a real-world ambulance allocation case study, combining SQL analysis with data visualization to generate actionable insights.
Review and Preview
- Consolidate your learning by reviewing key concepts: ER modeling, relational schema design, basic and advanced SQL techniques, and SQL-Python integration.