Data modeling is the practice of describing the data an organization cares about — the things it tracks, the facts it stores about those things, and the connections between them — before any tables are created in a database system. Think of it as the architectural drawing that comes before construction: it lets you reason about structure, catch problems, and agree with stakeholders on meaning while changes are still cheap.
In this course, data modeling is the bridge between a written problem description and a working relational database. It matters because design decisions made at the modeling stage ripple through everything that follows: query complexity, data integrity, redundancy, and how easily the system can grow. The topic also introduces the idea of levels of abstraction — conceptual, logical, and physical — which gives you a vocabulary for talking about the same data at different degrees of detail and independence from any particular DBMS.
As you watch the videos below, look for three things. First, note how each presenter defines a model and what they say it is for. Second, pay close attention to where the boundary falls between conceptual, logical, and physical descriptions, and what information is added at each step. Third, watch how modeling fits into an overall design workflow — where requirements come in and where implementation begins.
A short introductory explanation of what data modeling is and why organizations do it, aimed at learners new to the subject. Based on its title and the channel's teaching style, it should define the concept, give examples of what a model captures, and explain the role modeling plays in structuring data before it is stored or analyzed.
It is the natural starting point for the subtopics "What is Data Modeling?" and "Purpose and Importance of Data Modeling," and 365 Data Science produces clearly narrated, beginner-oriented explanations that avoid tool-specific jargon.
A focused five-minute comparison of conceptual and logical data models. The description notes that these two are easily confused when starting data projects, so expect the video to clarify what each level includes, what detail is deliberately left out, and when you would produce each one.
It directly supports the "Levels of Data Abstraction in Modeling" subtopic by sharpening the distinction students most often blur, and its tight length makes it easy to rewatch before working on your own models.
A practical walkthrough of the database design process from the Database Star channel, covering how to move from an idea or requirement toward a designed database. Expect it to show the sequence of steps a designer follows and how modeling decisions lead into the structures that are eventually implemented.
It places modeling in its real workflow context, supporting "Data Modeling as a Blueprint" and "Data Modeling in the Database Design Process," and Database Star is a well-established channel focused specifically on database design and SQL.
Together these videos establish the foundation the module's outcomes build on. The 365 Data Science overview and the Database Star walkthrough frame modeling as the blueprint stage of design, which is the setting in which you will later identify entities, attributes, and relationships (MO2, MO3) and construct a full ER diagram from business requirements (MO4). The conceptual-versus-logical comparison speaks most directly to MO1 by giving you a precise way to separate the levels of abstraction, and that same vocabulary is what makes it possible to critique a drafted diagram for missing or redundant elements later in the module (MO5).
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.