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IB Diploma – Computer Science

A rigorous, concept-driven course focusing on computational thinking, algorithms, and programming using Python or Java.

SL & HL Algorithms & Programming
✔ Python & Java programming
✔ Strong algorithmic thinking
✔ Project-based Internal Assessment
SL & HL
Options
35 hrs
IA Time
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IB Computer Science Laptop for i in range(n) if (x > y) while (true) def compute()

IBDP Computer Science – FAQs

Clear answers to help students understand the IB Computer Science curriculum

💻 What topics are covered in IBDP Computer Science? +
  • Computational thinking and system fundamentals
  • Algorithms, data structures, and programming
  • Databases, networks, and emerging technologies
🧠 What is the difference between SL and HL? +
  • HL includes deeper theoretical concepts
  • More complex problem-solving at HL level
  • SL focuses on core syllabus topics
🧪 What is the Internal Assessment (IA)? +
  • A practical project solving a real-world problem
  • Assesses planning, coding, and evaluation skills
  • Contributes significantly to final grades
🎓 How does IBDP CS help for university? +
  • Strong foundation for CS & Engineering
  • Builds analytical and problem-solving skills
  • Recognized by universities worldwide

IB Computer Science Highlights

Core concepts and skills across IB chapters

Swipe → to explore chapters

💾 A1: Computer Fundamentals

SL: 11h | HL: 18h
CPU architecture, GPUs, pipelining, memory, fetch–decode–execute cycle, OS, SaaS/PaaS, binary & hex, logic gates, truth tables, compilers & interpreters

📡 A2: Networks

SL: 11h | HL: 18h
Network devices, client–server vs P2P, topologies, TCP/IP, IPv4/IPv6, HTTP, DNS, switching, routing, cybersecurity, encryption, digital certificates

🗄 A3: Databases

SL: 11h | HL: 18h
Database schema, relational DBs, ER diagrams, normalization (1NF–3NF), SQL (joins, aggregates, views), transactions, data warehousing

🤖 A4: Machine Learning

SL: 5h | HL: 18h
Types of ML, preprocessing, regression, classification, clustering, reinforcement learning, genetic algorithms, neural networks (ANN, CNN), ethical issues

🧠 B1: Computational Thinking

SL & HL: 5h
Abstraction, decomposition, algorithm design, pattern recognition, tracing algorithms, flowcharts

💻 B2: Programming

SL: 40h | HL: 42h
Data types, selection & iteration, functions, arrays/lists, stacks, queues, recursion, sorting/searching algorithms, file handling, Big-O notation

🖥 B3: Object-Oriented Programming

SL: 7h | HL: 23h
Classes, objects, constructors, encapsulation, inheritance, polymorphism, abstract classes, composition, design patterns

🔘 B4: Abstract Data Structures (HL only)

Linked lists, binary search trees, hash maps, sets

📄 Case Study

SL: 15h | HL: 30h
Annually changing case study on emerging technology. SL: 2 research challenges, HL: 4 research challenges

Watch the video for details of the subject

Exam Paper Overview

Paper 1 – Computer Systems

Topics: A1–A4 + Case Study
SL: 1 hr 15 min, 50 marks | HL: 2 hrs, 80 marks

  • ✔ Weighting: SL 35%, HL 40%
  • ✔ Short-answer & structured questions
View Sample Paper

Paper 2 – Algorithms & Programming

Topics: B1–B4 | Separate Python/Java versions | Algorithmic thinking

  • ✔ Weighting: SL 35%, HL 40%
  • ✔ Scenario-based & practical questions
View Sample Paper

Internal Assessment (IA)

35 hours recommended | Student-designed project | Algorithm & development focus

  • ✔ Criteria A–E revised
  • ✔ ~2000 words + diagrams + 5-min video
  • ✔ Choice of programming language
View Sample IA

Why Choose TalentHome?

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Concept Clarity

Understand fundamentals with clear explanations and examples.

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Practical Coding

Hands-on coding exercises to build real-world skills.

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Guidance for school, college exams, and project work.

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Learn from highly experienced educators & professionals.

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