In this article we will have Introduction to Data Structures. Data is set of values. A single unit of value is called data item. Collection of data is usually organised into fields, records and files.
Data can be organised in different ways. The logical and mathematical model of a particular organization of data is called data structure. Data structures are generally classified into two types:
The data stored in data structure is accessed by means of operations. The choice of selecting data structure for particular situation depends upon the frequency operation. Some of the frequently used operations are:
Abstract Data Type (ADT) refers to set of data values and associated operations. ADT specifies the data type but is independent of its implementation details. Examples of ADT are stack, queues etc.
An algorithm is a well-defined sequence of steps used for solving certain sort of problems. The algorithm must be efficient in processing the data. The time and space are two major measures to judge the efficiency of an algorithm.
Complexity: The complexity of an algorithm is the function f(n) which determines the running time/space of algorithm in terms of input size ‘n’. It defines how fast or slow an algorithm is.
Time-Space Tradeoff: Time-space tradeoff means by increasing the amount of space required to store the data, the running time of algorithm decreases or by decreasing the amount of space required to store the data, the running time of algorithm increases.
There are generally three cases in complexity theory:
The Big O notation defines an upper bound function g(n) for function f(n) which represents the time/space complexity of an algorithm for input of size n. This is all we need to need know about Big O notation from IBPS IT officer exam perspective.
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