Applications Layer
IB Syllabus: Theme A, sections A3 (Databases) and A4 (Machine Learning)
The applications layer is where everything underneath becomes useful. Hardware executes instructions, the operating system juggles resources, and programs run, but what people actually experience is an application: something that stores their data, answers their questions, or learns from examples. The sections below cover two application domains, one about organising and querying data, the other about learning from it. They match the current IB CS (2027) alignment, and the layer has room to grow as the site covers more domains and curricula.
Sections in this layer
| # | Topic | Syllabus | Key Concepts | Level |
|---|---|---|---|---|
| 1 | Databases | A3 | Relational databases from first principles to SQL: schemas, ERDs, keys, normalisation to 3NF, queries and updates. HL adds transactions, views, alternative database types, data warehouses, and distributed databases. Complete, with a practice library. | SL + HL |
| 2 | Machine Learning | A4 | How machines learn from data: the types of machine learning and where they run (SL), then data preparation, supervised and unsupervised techniques, reinforcement learning, and neural networks (HL). In development. | SL + HL |
The ethical side of machine learning is already live: the syllabus statements on ML ethics (A4.4.1) and computing in daily life (A4.4.2) are covered in Ethics & Implications, because they are examined as discussion topics in their own right.
Want to build an application yourself?
Two parts of the site approach this layer from the maker’s side rather than the syllabus side:
- The JavaFX Cookbook is a recipe collection for building a desktop app in Java: tables, dialogs, file pickers, charts, and a SQLite connection when your app needs a real database.
- For IB students, the Internal Assessment is where it all comes together: a working application built for a real client. Even if you are not sitting IB exams, its planning and evaluation pages are a solid template for any serious software project.
Both live in their own sections (the cookbook under Programming, the IA under IB CS Assessments) because they are about building applications rather than the examined theory of this layer.
Suggested order
Work through Databases first. Machine learning builds directly on confident data handling, and several HL ML ideas (feature selection, training data) make far more sense once you have normalised and queried real datasets.
Learning Objectives
By the end of this layer, you should be able to:
- Explain the features, benefits, and limitations of relational databases, and design one normalised to 3NF (A3)
- Construct SQL queries and updates across multiple tables (A3)
- Describe the types of machine learning and their real-world applications and hardware requirements (A4)
- HL: evaluate advanced database approaches and explain core machine learning techniques, from regression to neural networks (A3/A4 HL)
Connections
- Programming Layer: data structures and OOP are the substrate every application is built from
- Information Layer: how the data that applications manage is actually encoded
- Ethics & Implications: what applications, especially learning ones, owe the people they affect