Unit 1. Introduction
Meaning of Data,
Data item,
Elementary and Group Data items,
Meaning of Data Structure,
Linear and Non Linear Data Structure,
Meaning of Algorithm,
Algorithm development.
Meaning of Data :
Data = Raw facts and figures.
We can define data as an elementary value or a collection of values.
For example- the Employee's name and ID are the data related to the Employee.
(Data is a collection of facts and figures or a set of values or values of a specific format
that refers to a single set of item values.)
Data item :
A Single unit of value is known as Data Item.
Elementary and Group Data items :
Elementary Items: Data Items that are unable to divide into sub-items are
known as Elementary Items. For example - the ID of an Employee.
Group Items: Data Items that have subordinate data items are known as Group
Items. For example - an employee's name can have a first, middle, and last
name.
Attribute and Entity : An entity is that which contains certain attributes or
properties, which may be assigned values.
Entity Set : Entities of similar attributes form an entity set.
Field : Field is a single elementary unit of information representing an attribute
of an entity.
Record : Record is a collection of field values of a given entity.
File : File is a collection of records of the entities in a given entity set.
, Meaning of Data Structure :
Definition: The logical or mathematical model of particular organization of data is
called Data Structure.
Explanation :
Data Structure is used to store and organize data on computer so that it can be accessed
and updated efficiently.
Characteristics of Data Structure:
1) Representation of Data: Data structures define a way of representing data in a
computer's memory, making it possible to store, manipulate, and access data
efficiently.
2) Access Techniques: Different data structures provide different techniques for
accessing data stored within them, such as random access or sequential access.
3) Storage Organization: Data structures define the organization of data in
memory, such as linear or hierarchical organization.
4) Insertion and Deletion Operations: Different data structures support different
methods for adding and removing elements, such as insertion at the end or
deletion from the front
5) Time and Space Complexity: Data structures can have different time and space
complexities, depending on the operations they support and the way they
organize data.
6) Adaptability: Some data structures are more adaptable to certain types of data
and operations than others. For example, a stack is more suitable for problems
that require Last-In-First-Out (LIFO) behavior, while a queue is better suited for
problems that require First-In-First-Out (FIFO) behavior.
7) Flexibility: Different data structures have different degrees of flexibility, such as
the ability to dynamically grow or shrink in size, or the ability to efficiently insert
or delete elements in the middle.
Advantages of Data Structure:
1) Better Data Organization: Data structures provide a way of organizing data in a
meaningful and efficient manner, making it easier to access and manipulate data.
2) Increased Data Retrieval Efficiency: Data structures can provide fast and
efficient retrieval of data, which is essential in many real-world applications.
3) Efficient Data Manipulation: Data structures can provide efficient methods for
adding, deleting, and modifying data, which is important in dynamic applications.
4) Improved Code Reusability: Reusable data structures can be used in many
different applications, reducing the time and effort required to write and maintain
code.
5) Better Problem-Solving Capability: Data structures provide a way of modeling
real-world problems and solving them in a more efficient and elegant manner.
Meaning of Data,
Data item,
Elementary and Group Data items,
Meaning of Data Structure,
Linear and Non Linear Data Structure,
Meaning of Algorithm,
Algorithm development.
Meaning of Data :
Data = Raw facts and figures.
We can define data as an elementary value or a collection of values.
For example- the Employee's name and ID are the data related to the Employee.
(Data is a collection of facts and figures or a set of values or values of a specific format
that refers to a single set of item values.)
Data item :
A Single unit of value is known as Data Item.
Elementary and Group Data items :
Elementary Items: Data Items that are unable to divide into sub-items are
known as Elementary Items. For example - the ID of an Employee.
Group Items: Data Items that have subordinate data items are known as Group
Items. For example - an employee's name can have a first, middle, and last
name.
Attribute and Entity : An entity is that which contains certain attributes or
properties, which may be assigned values.
Entity Set : Entities of similar attributes form an entity set.
Field : Field is a single elementary unit of information representing an attribute
of an entity.
Record : Record is a collection of field values of a given entity.
File : File is a collection of records of the entities in a given entity set.
, Meaning of Data Structure :
Definition: The logical or mathematical model of particular organization of data is
called Data Structure.
Explanation :
Data Structure is used to store and organize data on computer so that it can be accessed
and updated efficiently.
Characteristics of Data Structure:
1) Representation of Data: Data structures define a way of representing data in a
computer's memory, making it possible to store, manipulate, and access data
efficiently.
2) Access Techniques: Different data structures provide different techniques for
accessing data stored within them, such as random access or sequential access.
3) Storage Organization: Data structures define the organization of data in
memory, such as linear or hierarchical organization.
4) Insertion and Deletion Operations: Different data structures support different
methods for adding and removing elements, such as insertion at the end or
deletion from the front
5) Time and Space Complexity: Data structures can have different time and space
complexities, depending on the operations they support and the way they
organize data.
6) Adaptability: Some data structures are more adaptable to certain types of data
and operations than others. For example, a stack is more suitable for problems
that require Last-In-First-Out (LIFO) behavior, while a queue is better suited for
problems that require First-In-First-Out (FIFO) behavior.
7) Flexibility: Different data structures have different degrees of flexibility, such as
the ability to dynamically grow or shrink in size, or the ability to efficiently insert
or delete elements in the middle.
Advantages of Data Structure:
1) Better Data Organization: Data structures provide a way of organizing data in a
meaningful and efficient manner, making it easier to access and manipulate data.
2) Increased Data Retrieval Efficiency: Data structures can provide fast and
efficient retrieval of data, which is essential in many real-world applications.
3) Efficient Data Manipulation: Data structures can provide efficient methods for
adding, deleting, and modifying data, which is important in dynamic applications.
4) Improved Code Reusability: Reusable data structures can be used in many
different applications, reducing the time and effort required to write and maintain
code.
5) Better Problem-Solving Capability: Data structures provide a way of modeling
real-world problems and solving them in a more efficient and elegant manner.
