Summary
Highlights
The lecture begins by defining non-primitive data structures. These structures are essential for organizing, processing, storing, and accessing data effectively. They provide the framework to manage and retrieve data efficiently as needed.
Data structures are broadly classified into six main categories: Linear, Non-Linear, Homogeneous, Non-Homogeneous (or Heterogeneous), Static (or Physical), and Dynamic (or Logical). Each classification serves a distinct purpose in how data is organized and managed.
Linear data structures organize data sequentially, one after another, like arrays and queues. Non-linear data structures, such as trees and graphs, store data without a sequential order, allowing for more complex relationships between data elements.
Homogeneous data structures store data of the same type (e.g., an array of integers), while non-homogeneous (or heterogeneous) data structures can store data of different types simultaneously (e.g., structures that combine integers, floats, and characters).
Static data structures have a fixed size determined at compile time (e.g., arrays), involving early binding of memory. Dynamic data structures, on the other hand, allow for flexible size allocation at runtime (e.g., linked lists), supporting late binding for memory allocation and deallocation.