IB DP Computer Science DP
SL & HL  ·  2027 first-exam curriculum

All higher level knowledge base articles

A1.1.3 Explain the differences between the CPU and the GPU. (HL only) HL
A1.1.6 Describe the process of pipelining in multi-core architectures. (HL only). HL
A1.3.5 Explain the role of the operating system in managing multitasking and resource allocation. (HL only) . HL
A1.3.6 Describe the use of the control system components. (HL only). HL
A1.3.7 Explain the use of control systems in a range of real-world applications. (HL only). HL
A1.4.1 Evaluate the translation processes of interpreters and compilers. (HL only). HL
A2.1.5 Describe the function of the TCP/IP model. (HL only) . HL
A2.2.2 Describe the function of servers. (HL only). HL
A2.3.4 Explain how static routing and dynamic routing move data across local area networks. (HL only) HL
A2.4.2 Describe common network vulnerabilities. (HL only) HL
A2.4.3 Describe common network countermeasures. (HL only) HL
A3.3.4 Construct calculations within a database using SQL’s aggregate functions. (HL only) HL
A3.3.5 Describe different database views. (HL only) HL
A3.3.6 Describe how transactions maintain data integrity in a database. (HL only) HL
A3.4.1 Outline the different types of databases as approaches to storing data. (HL only) HL
A3.4.2 Explain the primary objectives of data warehouses in data management and business intelligence. (HL only) HL
A3.4.3 Explain the role of online analytical processing (OLAP) and data mining for business intelligence. (HL only) HL
A3.4.4 Describe the features of distributed databases. (HL only) HL
A4.2.1 Describe the significance of data cleaning. (HL only) HL
A4.2.2 Describe the role of feature selection. (HL only) HL
A4.2.3 Describe the importance of dimensionality reduction. (HL only) HL
A4.3.1 Explain how linear regression is used to predict continuous outcomes. (HL only) HL
A4.3.2 Explain how classifications techniques in supervised learning are used to predict discrete categorical outcomes. (HL only) HL
A4.3.3 Explain the role of hyperparameter tuning when evaluating supervised learning algorithms. (HL only) HL
A4.3.4 Describe how clustering techniques in unsupervised learning are used to group data based on similarities in features.  (HL only) HL
A4.3.5 Describe how learning techniques using the association rule are used to uncover relations between different attributes in large data sets. (HL only) HL
A4.3.6 Describe how an agent learns to make decisions by interacting with its environment in reinforcement learning. (HL only) HL
A4.3.7 Describe the application of genetic algorithms in various real-world situations. (HL only) HL
A4.3.8 Outline the structure and function of ANNs and how multi-layer networks are used to model complex patterns in data sets. (HL only) HL
A4.3.9 Describe how CNNs are designed to adaptively learn spatial hierarchies of features in images. (HL only) HL
A4.3.10 Explain the importance of model selection and comparison in machine learning. (HL only) HL
B2.4.4 Explain the fundamental concept of recursion and its applications in programming. (HL only) HL
B2.4.5 Construct and trace recursive algorithms in a programming language. (HL only) HL
B3.2.1 Explain and apply the concept of inheritance in OOP to promote code reusability. (HL only) HL
B3.2.2 Construct code to model polymorphism and its various forms, such as method overriding. (HL only) HL
B3.2.3 Explain the concept of abstraction in OOP. (HL only) HL
B3.2.4 Explain the role of composition and aggregation in class relationships. (HL only) HL
B3.2.5 Explain commonly used design patterns in OOP. (HL only) HL
B4.1.1 Explain the properties and purpose of ADTs in programming. (HL only) HL
B4.1.2 Evaluate linked lists. (HL only) HL
B4.1.3 Construct and apply linked lists: singly, doubly and circular. (HL only) HL
B4.1.4 Explain the structures and properties of BSTs. (HL only) HL
B4.1.5 Construct and apply sets as an ADT. (HL only) HL
B4.1.6 Explain the core principles of ADTs. (HL only) HL