Learn how to design clear, structured data models using real-world examples and simple visuals-no experience needed!

What you'll learn

Understand foundational data modeling concepts such as entities, attributes, relationships, and keys.

Differentiate between conceptual, logical, and physical data models, and understand when to use each.

Draw ER diagrams using Crow's Foot notation to represent real-world scenarios.

Build normalized, well-structured data models that reflect business rules and avoid redundancy.

Design dimensional models (Star/Snowflake schemas) to support analytics and reporting.

Requirements

Basic understanding of what a database is will help

A curious mind and interest in solving real-world problems with data

Access to internet

You do NOT need any programming or database experience to take this course

Description

Welcome to the Complete Beginner's Guide to Data Modeling—designed to help you organize, structure, and connect data with confidence.If you're new to the world of data and systems, and you're wondering how information is structured behind the scenes in apps, websites, reports, or databases—this course is built for you.Data Modeling for Beginners course is a simple, practical, and visual introduction to the world of data modeling. No prior experience is needed. You'll start from the ground up and gradually build the knowledge and confidence to model real-world data systems on your own.Whether you're preparing for a role in data analysis, business intelligence, or software development—or you're simply curious about how data flows and connects—this course will help you think like a data modeler and communicate in the language of modern data.Most data-related courses dive straight into tools or coding. But this course starts with the thinking process behind great data systems—what to model, why, and how to connect it all.Understanding how to model data is a core skill for anyone working in the digital world. Every app, website, dashboard, and report is powered by structured data models.With data modeling skills, you can:Design smarter systems.Avoid bad data practices like duplication and inconsistency.Communicate better with developers and analysts.Make informed decisions when managing or analyzing data.Prepare for interviews and real-world data challenges.Data modeling is not just a technical skill—it's a way of thinking clearly and structurally about problems.In this course:You'll learn using real-world scenarios—not just theoryWe explain every term and concept using plain EnglishEach section is supported by visual diagrams and examplesYou'll create your own models using free, browser-based toolsYou'll gain practical skills for designing systems used in analytics, reporting, and applicationsWe don't just teach how to model—we show you why it matters and how to use it in your own context.After finishing this course, you'll be able to:Explain what data modeling is—and why it's essential in modern systemsIdentify entities, attributes, and relationships from business scenariosChoose the right relationship types (1:1, 1:M, M:N) based on the dataApply best practices when structuring tables and diagramsUse Crow's Foot notation and other visual methods to document your modelsUnderstand and apply basic normalization to clean up data structureMove from conceptual to logical to physical modeling with easeRead, evaluate, and improve existing data modelsDesign dimensional models (like Star Schemas) for analytical use casesThis course is:Beginner-friendly: Built specifically for learners with zero technical backgroundStep-by-step: Each concept builds on the last with clear progressionVisual: Diagrams, examples, and mini-projects make everything clickHands-on: You'll build models yourself, not just watch lecturesThis course comes with a 30-day money-back guarantee, so there's absolutely no risk. If you're not satisfied for any reason, get a full refund within 30 days, Udemy's refund policy applies.

Overview

Section 1: Introduction

Lecture 1 Introduction To This Course

Section 2: Introduction to Data Modeling Fundamentals

Lecture 2 What is Data Modeling?

Lecture 3 Why Data Modeling matters (real-world examples)

Lecture 4 Overview of transactional vs. analytical data modeling

Lecture 5 Data modeling vs. database design

Section 3: Basic Data Modeling Concepts & Terminology

Lecture 6 What is an Entity, Attribute, and Relationship?

Lecture 7 Requirement For Choosing Attributes

Lecture 8 Strong vs. Weak Entities, Tables = Entities, Columns = Attributes

Lecture 9 Primary Key & Foreign Key

Lecture 10 Build Relationships Between Entities (One-to-One, One-to-Many, Many-to-Many)

Lecture 11 What Is Multi-Valued Attributes

Section 4: Building Blocks of a Data Model

Lecture 12 Identify entities and attributes

Lecture 13 Create Tables and Add Attributes

Lecture 14 Multi-Valued Attributes and How to Handle Them

Lecture 15 Summarize: how to structure a basic data model

Section 5: Understanding Relationships & Cardinality

Lecture 16 What Are Entity Relationships (ERD) in Data Modeling?

Lecture 17 What is Cardinality?

Lecture 18 max/min values explained

Lecture 19 Why Real-World Complexities Matter

Lecture 20 Build Relationships (with visuals)

Lecture 21 Chen Notations

Lecture 22 Crow's Foot Notation Basics

Lecture 23 Complex Relationships in Practice

Section 6: Real-World Modeling: Entity & Attribute Constraints

Lecture 24 Attribute constraints (data types, required fields)

Lecture 25 Entity hierarchies (e.g., Employee -> Manager)

Lecture 26 Cross-entity dependencies (weak entities with FK reliance)

Lecture 27 Summary of modeling complex real-world scenarios

Section 7: Navigate Methodologies, Techniques, and Notations

Lecture 28 UML : Why It Matters in Data Modeling

Lecture 29 Overview of ER, UML

Lecture 30 ER, UML, Crow's Foot notation Choosing the right technique

Lecture 31 Visual demo using dbdiagram.io or draw.io

Section 8: Working with Different Levels of a Data Model

Lecture 32 Conceptual vs Logical vs Physical Models

Lecture 33 Forward-engineering: from conceptual to physical

Lecture 34 Reverse-engineering: from database to ERD

Lecture 35 What is Normalizations?

Lecture 36 Different Types of Anomalies (Insert, Update, Delete)

Lecture 37 How to solve This Data Issues?

Lecture 38 Introductions to normalization-1NF

Lecture 39 Introductions to normalization-2NF

Lecture 40 Introductions to normalization-3NF

Section 9: Dimensional Modeling Basics (for Analytics)

Lecture 41 Star Schema vs Snowflake Schema

Lecture 42 Fact tables vs Dimension tables

Lecture 43 Use case: Sales dashboard-Visualize a simple star schema with a fact table

Section 10: Practice Data Modeling

Section 11: Bonus

Lecture 44 Bonus Lecture

Absolute beginners who are curious about how data is structured,Aspiring data analysts, data engineers, or BI professionals looking for a clear starting point in modeling,Students and career changers entering the data or tech industry,Non-technical professionals (project managers, business users, etc.) who want to understand how data is organized in systems,Developers and designers who want to improve their understanding of data flow and structure,Data Analysts – who want to understand how the data they analyze is structured,Data Engineers – who want to design robust and scalable data models,Database Developers – who want to improve schema design and documentation,BI Professionals – who want to strengthen their dimensional modeling skills