Understanding the IB Computer Science syllabus offers valuable insight into the skills and knowledge you'll gain during the course. Join us as we break down the key elements of the syllabus to help you better understand what lies ahead in your journey!

The IB Computer Science syllabus consists of two main themes – Concepts of computer science , and Computational thinking and problem-solving .

Computer Science

For the first theme, students study four topics (with additional material for HL students) covering the abstract ideas of how computing systems operate.

Concepts of computer science

Students explore the internal components of a computer, such as the CPU's interaction with memory and the role of specialized hardware like GPUs.

Computer hardware and operation

This area covers how data is encoded using binary and hexadecimal systems and how logic gates are used to process that information.

Data representation and computer logic

Learners examine how operating systems manage resources and multitasking, while HL students also study control system components like sensors and actuators.

Operating systems and control systems

HL students evaluate the technical processes used by compilers and interpreters to translate high-level code into machine-readable instructions.

Translation (HL only)

This subtopic identifies the various types of networks, such as LANs and VPNs, and the purpose of modern digital infrastructures like cloud computing.

Network fundamentals

Students investigate different network topologies and networking models, with HL students diving deeper into the specific functions of various servers.

Network architecture

This covers the mechanisms of moving data, including IP addressing, packet switching, and the physical media used for transmission.

Data transmissions

Students discuss methods for protecting networks, including the use of firewalls, encryption, and identifying common vulnerabilities and countermeasures.

Network security

Learners explore the essential features and benefits of relational databases, such as data integrity and reduced redundancy.

Database fundamentals

This area focuses on creating organized database structures through schemas, entity-relationship diagrams (ERDs), and the process of normalization.

Database design

Students learn to use SQL for defining and manipulating data, including constructing complex queries to retrieve information from multiple tables.

Database programming

HL students investigate non-relational database models and the role of data warehouses in large-scale business intelligence.

Alternative databases and data warehouses (HL only)

This topic introduces students to various machine learning approaches, such as supervised and unsupervised learning, and their real-world applications.

Machine learning fundamentals

HL students learn how to prepare data for models by cleaning it, selecting relevant features, and reducing its complexity.

Data preprocessing (HL only)

This subtopic details advanced techniques HL students must understand, including neural networks, genetic algorithms, and reinforcement learning.

Machine learning approaches (HL only)

All students must discuss the profound ethical implications of machine learning, focusing on issues like algorithmic bias, privacy, and societal impact.

Ethical considerations

The second theme focuses on applying practical skills to solve problems through the process of computational thinking.

Computational thinking and problem-solving

Students learn to specify problems clearly and apply core concepts like abstraction and decomposition to find solutions without necessarily using code.

Approaches to computational thinking

This covers the basics of writing and tracing code, including managing variables, data types, and handling potential errors.

Programming fundamentals

Learners compare static and dynamic data structures and implement fundamental types like arrays, lists, stacks, and queues.

Data structures

This subtopic focuses on using the correct sequence of instructions, selection structures, and looping to control program flow effectively.

Programming constructs

Students implement and analyze the efficiency of common algorithms for searching and sorting data using Big O notation.

Programming algorithms

Learners develop the ability to read from and write to sequential text files within their programs.

File processing

Students learn the core principles of the object-oriented paradigm, such as using classes and objects to model real-world entities.

Fundamentals of OOP for a single class

HL students explore advanced OOP concepts like inheritance, polymorphism, and common design patterns.

Fundamentals of OOP for multiple classes (HL only)

HL students evaluate and implement complex data structures, including linked lists and binary search trees.

Fundamentals of ADTs

A4.1 Machine learning fundamentals | IB Computer Science Syllabus

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Machine learning fundamentals

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