What does data abstraction primarily focus on in computer science?

Data abstraction primarily focuses on hiding implementation details, which means emphasizing the essential properties and features of the data while concealing the complexities of how those features are implemented. This approach allows programmers to work with data at a higher level without needing to understand the intricate inner workings of the system or the specific algorithms used.

The purpose of data abstraction is to simplify interactions with complex systems, making it easier to manage, utilize, and manipulate data. By isolating the core functionality from the underlying code, developers can create more modular, maintainable, and reusable code. This is particularly beneficial in software design, where different layers of a system can interact without being tightly coupled to one another.

In contrast, the other options pertain to different aspects of computer science and system design, such as optimizing speed, expanding capacity, or enhancing hardware performance, which are important but not the primary focus of data abstraction.