1)
a) Define data type and
abstract data type (ADT).

Data Type is a particular kind of data item, as defined by the
values it can take, the programming language used, or the operations that can
be performed on it. It is an attribute of data which tells the compiler or
interpreter how the programmer intends to use the data. Most programming
languages support common data types of real, integer and Boolean. Abstract Data
type (ADT) is a type (or class) for objects whose behaviour is defined by a set
of value and a set of operations. An ADT can as well be defined as a
mathematical model for data types, where a data type is defined by its
behaviour from the point of view of a user of the data, specifically in terms
of possible values, possible operations on data of this type, and the behaviour
of these operations. So, the key difference is that a data type represents
encapsulation, and ADT represents abstraction.

b) Give an example of an
abstract data type.

In Abstract Data Type
(ADT) only behaviour is defined but not implementation. On the other hand,
opposite of ADT is Concrete Data Type (CDT), which contains an implementation
of ADT. The examples of ADT are Array, List, Map, Queue, Set, Stack, Table,
Tree, and Vector.

c) List five benefits of
using ADTs, giving a short explanation of each.

i. ADT is reusable,
robust, and is based on principles of Object Oriented Programming (OOP) and
Software Engineering (SE)

ii. An ADT can be re-used
at several places and it reduces coding efforts

iii. Encapsulation ensures
that data cannot be corrupted

iv. Working of various
integrated operation cannot be tampered with by the application program

v. ADT ensures a robust
data structure

2)
With
the help of graph explain different asymptotic notations Give example for each.

Asymptotic Notations are the expressions
that are used to represent the complexity of an algorithm. There are three
types of analysis that are performed on a particular algorithm. Asymptotic
notations are used to make meaningful statements about the efficiency of the
algorithm. Asymptotic notation helps us to make approximate but meaningful
assumption about the time and the space complexity.

i. Best
Case: In which the performance of an algorithm for the input is an analysed,
for which the algorithm takes less time or space.

ii. Worst
Case: In which performance of an algorithm for the input is analysed, for which
the algorithm takes long time or space.

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