Data Structures: Arrangement of data in main memory so that they (data items) can be used efficiently.
Algorithms: Sequence of steps on data using efficient data structures to solve a given problem.
Some other Terminology
Database: Collection of information in permanent storage for faster retrieval and updation.
Data Warehousing: Management of huge amount of legacy data for better analysis.
Big data: Analysis of too large or complex data when cannot be deal with traditional data processing application.
Classification of Data Structure • Linear data structure: Data structure in which data elements are arranged sequentially or linearly, where each element is attached to its previous and next adjacent elements, is called a linear data structure.
Examples of linear data structures are array, stack, queue, linked list, etc.
• Static data structure: Static data structure has a fixed memory size. It is easier to access the elements in a static data structure. An example of this data structure is an array.
• Dynamic data structure: In dynamic data structure, the size is not fixed. It can be randomly updated during the runtime which may be considered efficient concerning the memory (space) complexity of the code.
Examples of this data structure are queue, stack, etc.
• Non-linear data structure: Data structures where data elements are not placed sequentially or linearly are called non-linear data structures. In a non-linear data structure, we can’t traverse all the elements in a single run only.
Examples of non-linear data structures are trees and graphs.