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This document discusses data representation and data structures. It defines data as numbers, alphabets, and symbols. Data can be atomic, consisting of a single element, or composite, consisting of multiple elements. The document also defines data types in programming languages like C which specify the kind of data variables can hold. User-defined types and derived data types are also discussed. Data structures are defined as the organization of data and relationships between data elements that allow for methods of storage, access, and processing. Common data structures include arrays, stacks, queues, linked lists, trees, and graphs.
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Data structure & its types
This document defines and describes different types of data structures. It first defines a data structure as a logical way to store and retrieve data. It then describes common data structure operations like traversing, searching, insertion and deletion. The main types discussed are linear data structures like arrays, linked lists, stacks and queues, as well as non-linear structures like trees and graphs. Specific linear structures are defined and their representations and operations are outlined. Non-linear structures like trees and graphs are also introduced along with common operations.
STACKS IN DATASTRUCTURE
This document discusses stacks as a linear data structure. It defines a stack as a last-in, first-out (LIFO) collection where the last item added is the first removed. The core stack operations of push and pop are introduced, along with algorithms to insert, delete, and display items in a stack. Examples of stack applications include reversing strings, checking validity of expressions with nested parentheses, and converting infix notation to postfix.
Data Structure (Queue)
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
Queue data structure
A queue is a non-primitive linear data structure that follows the FIFO (first-in, first-out) principle. Elements are added to the rear of the queue and removed from the front. Common operations on a queue include insertion (enqueue) and deletion (dequeue). Queues have many real-world applications like waiting in lines and job scheduling. They can be represented using arrays or linked lists.
Sorting Algorithms
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
Introduction to data structure ppt
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. https://apkleet.com
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ER-Model-ER Diagram
Here are the entity relationship diagrams and tables for the car insurance company database:
(a) E-R Diagram:
Person - owns - Car
(1:M)
Car - involved in - Accident
(0:M)
(b) Tables:
person
(driver_id primary key,
name, address)
car
(license primary key,
year, model, driver_id foreign key references person)
accident
(report_number primary key,
date, location)
participated
(driver_id foreign key references person,
license foreign key references car,
report_number foreign key references accident
Tree - Data Structure
This document provides an overview of trees as a non-linear data structure. It begins by discussing how trees are used to represent hierarchical relationships and defines some key tree terminology like root, parent, child, leaf, and subtree. It then explains that a tree consists of nodes connected in a parent-child relationship, with one root node and nodes that may have any number of children. The document also covers tree traversal methods like preorder, inorder, and postorder traversal. It introduces binary trees and binary search trees, and discusses operations on BSTs like search, insert, and delete. Finally, it provides a brief overview of the Huffman algorithm for data compression.
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Data structure & its types
This document defines and describes different types of data structures. It first defines a data structure as a logical way to store and retrieve data. It then describes common data structure operations like traversing, searching, insertion and deletion. The main types discussed are linear data structures like arrays, linked lists, stacks and queues, as well as non-linear structures like trees and graphs. Specific linear structures are defined and their representations and operations are outlined. Non-linear structures like trees and graphs are also introduced along with common operations.
STACKS IN DATASTRUCTURE
This document discusses stacks as a linear data structure. It defines a stack as a last-in, first-out (LIFO) collection where the last item added is the first removed. The core stack operations of push and pop are introduced, along with algorithms to insert, delete, and display items in a stack. Examples of stack applications include reversing strings, checking validity of expressions with nested parentheses, and converting infix notation to postfix.
Data Structure (Queue)
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
Queue data structure
A queue is a non-primitive linear data structure that follows the FIFO (first-in, first-out) principle. Elements are added to the rear of the queue and removed from the front. Common operations on a queue include insertion (enqueue) and deletion (dequeue). Queues have many real-world applications like waiting in lines and job scheduling. They can be represented using arrays or linked lists.
Sorting Algorithms
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
Introduction to data structure ppt
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. https://apkleet.com
<a href="https://apkleet.com" >games apk </a>
ER-Model-ER Diagram
Here are the entity relationship diagrams and tables for the car insurance company database:
(a) E-R Diagram:
Person - owns - Car
(1:M)
Car - involved in - Accident
(0:M)
(b) Tables:
person
(driver_id primary key,
name, address)
car
(license primary key,
year, model, driver_id foreign key references person)
accident
(report_number primary key,
date, location)
participated
(driver_id foreign key references person,
license foreign key references car,
report_number foreign key references accident
Tree - Data Structure
This document provides an overview of trees as a non-linear data structure. It begins by discussing how trees are used to represent hierarchical relationships and defines some key tree terminology like root, parent, child, leaf, and subtree. It then explains that a tree consists of nodes connected in a parent-child relationship, with one root node and nodes that may have any number of children. The document also covers tree traversal methods like preorder, inorder, and postorder traversal. It introduces binary trees and binary search trees, and discusses operations on BSTs like search, insert, and delete. Finally, it provides a brief overview of the Huffman algorithm for data compression.
Queue ppt
The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.
Array ppt
This document discusses arrays in three sentences or less:
Arrays allow storing and accessing multiple values under a single name, with each value stored in consecutive memory locations. Arrays come in one-dimensional, two-dimensional, and multi-dimensional forms and can be accessed using indexes. Common array operations include initialization, accessing elements, searching, sorting, and performing operations on all elements using loops.
Introduction to stack
Stacks are linear data structures that only allow insertion and deletion of elements from one end, called the top. Elements are inserted via a push operation and deleted via a pop operation. Stacks can be implemented using arrays, with a pointer tracking the top element. Common applications of stacks include reversing the order of elements and evaluating mathematical expressions by treating them as a postfix notation.
Linked list
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Data Structures
The document discusses data structures and arrays. It begins by defining data, data structures, and how data structures affect program design. It then categorizes data structures as primitive and non-primitive. Linear and non-linear data structures are described as examples of non-primitive structures. The document focuses on arrays as a linear data structure, covering array declaration, representation in memory, calculating size, types of arrays, and basic operations like traversing, searching, inserting, deleting and sorting. Two-dimensional arrays are also introduced.
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The document discusses stacks and queues as abstract data types. It describes their basic operations and implementations using arrays. Stacks follow LIFO (last-in, first-out) order and can be used for applications like undo operations. Queues follow FIFO (first-in, first-out) order and can be used where ordering of elements is important, like in printing queues. The document also discusses infix, prefix and postfix notations for arithmetic expressions and provides an algorithm to convert infix to postfix notation using a stack. Finally, it describes different types of queues including linear and circular queues.
stack presentation
The document introduces stacks and discusses their implementation and applications. It defines a stack as a data structure that follows LIFO order, where elements can only be added and removed from one end. Stacks have two main implementations - using arrays and linked lists. Common applications of stacks include undo/redo in editors, browser history, and evaluating postfix expressions.
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The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
Introduction to data structure
This document provides an introduction to data structures. It defines data structures as a way of organizing data so that it can be used efficiently. The document then discusses basic terminology, why data structures are important, how they are studied, and how they are classified as simple or compound, and linear or non-linear. It proceeds to describe common data structures like arrays, stacks, queues, linked lists, trees, and graphs, and how they support basic operations. The document concludes by discussing how to select an appropriate data structure based on the problem constraints and required operations.
Stacks in c++
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
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- Arrays in C++ must be declared before use, specifying the type, name, and number of elements. Elements are accessed using an index.
- The main advantages of arrays are that they allow storing and processing large numbers of values efficiently using a single name. Arrays also make sorting and searching values easier.
Tree in data structure
1) Trees are hierarchical data structures that store data in nodes connected by edges. They are useful for representing hierarchical relationships.
2) Binary search trees allow quick search, insertion, and deletion of nodes due to the organizational property that the value of each node is greater than all nodes in its left subtree and less than all nodes in its right subtree.
3) Common tree traversal algorithms include preorder, inorder, and postorder traversals, which visit nodes in different orders depending on whether the left/right children or root is visited first.
stack & queue
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
List,tuple,dictionary
The document discusses Python's four main collection data types: lists, tuples, sets, and dictionaries. It provides details on lists, including that they are ordered and changeable collections that allow duplicate members. Lists can be indexed, sliced, modified using methods like append() and insert(), and have various built-in functions that can be used on them. Examples are provided to demonstrate list indexing, slicing, changing elements, adding elements, removing elements, and built-in list methods.
Counting Sort
Counting sort is an algorithm that sorts elements by counting the number of occurrences of each unique element in an array. It works by:
1) Creating a count array to store the count of each unique object in the input array.
2) Modifying the count array to store cumulative counts.
3) Creating an output array by using the modified count array to output elements in sorted order.
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AVL Tree
An AVL tree is a self-balancing binary search tree that guarantees search, insertion, and deletion operations will take O(log n) time on average. It achieves this by ensuring the heights of the left and right subtrees of every node differ by at most one. When an insertion or deletion causes a height imbalance of two, rotations are performed to rebalance the tree.
Lecture 1 data structures and algorithms
This document discusses data structures and their applications. It defines key terms like data, data item, entity, attribute, field, record, and file. It explains that a data structure is a logical organization of data that specifies the data elements and operations that can be performed on them. Common operations include traversing, searching, inserting, and deleting. The choice of data structure depends on how frequently certain operations will be performed. Real-life data manipulation requires storage, retrieval, and transformation of user data.
Binary Search
This document describes binary search and provides an example of how it works. It begins with an introduction to binary search, noting that it can only be used on sorted lists and involves comparing the search key to the middle element. It then provides pseudocode for the binary search algorithm. The document analyzes the time complexity of binary search as O(log n) in the average and worst cases. It notes the advantages of binary search are its efficiency, while the disadvantage is that the list must be sorted. Applications mentioned include database searching and solving equations.
Linked List
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
Database
Here is a proposed design for a University database system:
Entities:
- Students
- Faculty
- Courses
- Classes
- Degrees
- Grades
Tables:
- Students (student_id, name, address, etc.)
- Faculty (faculty_id, name, department, etc.)
- Courses (course_id, name, description, etc.)
- Classes (class_id, course_id, faculty_id, semester, year)
- Degrees (degree_id, name)
- Grades (student_id, class_id, grade)
Relationships:
- Students take Classes
- Faculty teach Classes
1. Introduction to Data Structure.pptx
This document provides an introduction to data structures. It defines data as distinct pieces of information that can exist in various forms, such as numbers, text, bits and bytes. It defines data structure as a way to organize data so it can be used efficiently. Common data structures include arrays, lists, stacks, queues and files. The document outlines some important data types like integer, boolean, floating, character and string. It also discusses basic operations on data structures like traversing, searching, inserting, deleting, sorting and merging. Finally, it provides examples of different data structures and their applications.
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Queue ppt
The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.
Array ppt
This document discusses arrays in three sentences or less:
Arrays allow storing and accessing multiple values under a single name, with each value stored in consecutive memory locations. Arrays come in one-dimensional, two-dimensional, and multi-dimensional forms and can be accessed using indexes. Common array operations include initialization, accessing elements, searching, sorting, and performing operations on all elements using loops.
Introduction to stack
Stacks are linear data structures that only allow insertion and deletion of elements from one end, called the top. Elements are inserted via a push operation and deleted via a pop operation. Stacks can be implemented using arrays, with a pointer tracking the top element. Common applications of stacks include reversing the order of elements and evaluating mathematical expressions by treating them as a postfix notation.
Linked list
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Data Structures
The document discusses data structures and arrays. It begins by defining data, data structures, and how data structures affect program design. It then categorizes data structures as primitive and non-primitive. Linear and non-linear data structures are described as examples of non-primitive structures. The document focuses on arrays as a linear data structure, covering array declaration, representation in memory, calculating size, types of arrays, and basic operations like traversing, searching, inserting, deleting and sorting. Two-dimensional arrays are also introduced.
Stacks and Queue - Data Structures
The document discusses stacks and queues as abstract data types. It describes their basic operations and implementations using arrays. Stacks follow LIFO (last-in, first-out) order and can be used for applications like undo operations. Queues follow FIFO (first-in, first-out) order and can be used where ordering of elements is important, like in printing queues. The document also discusses infix, prefix and postfix notations for arithmetic expressions and provides an algorithm to convert infix to postfix notation using a stack. Finally, it describes different types of queues including linear and circular queues.
stack presentation
The document introduces stacks and discusses their implementation and applications. It defines a stack as a data structure that follows LIFO order, where elements can only be added and removed from one end. Stacks have two main implementations - using arrays and linked lists. Common applications of stacks include undo/redo in editors, browser history, and evaluating postfix expressions.
SEARCHING AND SORTING ALGORITHMS
The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
Introduction to data structure
This document provides an introduction to data structures. It defines data structures as a way of organizing data so that it can be used efficiently. The document then discusses basic terminology, why data structures are important, how they are studied, and how they are classified as simple or compound, and linear or non-linear. It proceeds to describe common data structures like arrays, stacks, queues, linked lists, trees, and graphs, and how they support basic operations. The document concludes by discussing how to select an appropriate data structure based on the problem constraints and required operations.
Stacks in c++
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
Array in c++
- An array is a collection of consecutive memory locations that all have the same name and type. An array allows storing multiple values of the same type using a single name.
- Arrays in C++ must be declared before use, specifying the type, name, and number of elements. Elements are accessed using an index.
- The main advantages of arrays are that they allow storing and processing large numbers of values efficiently using a single name. Arrays also make sorting and searching values easier.
Tree in data structure
1) Trees are hierarchical data structures that store data in nodes connected by edges. They are useful for representing hierarchical relationships.
2) Binary search trees allow quick search, insertion, and deletion of nodes due to the organizational property that the value of each node is greater than all nodes in its left subtree and less than all nodes in its right subtree.
3) Common tree traversal algorithms include preorder, inorder, and postorder traversals, which visit nodes in different orders depending on whether the left/right children or root is visited first.
stack & queue
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
List,tuple,dictionary
The document discusses Python's four main collection data types: lists, tuples, sets, and dictionaries. It provides details on lists, including that they are ordered and changeable collections that allow duplicate members. Lists can be indexed, sliced, modified using methods like append() and insert(), and have various built-in functions that can be used on them. Examples are provided to demonstrate list indexing, slicing, changing elements, adding elements, removing elements, and built-in list methods.
Counting Sort
Counting sort is an algorithm that sorts elements by counting the number of occurrences of each unique element in an array. It works by:
1) Creating a count array to store the count of each unique object in the input array.
2) Modifying the count array to store cumulative counts.
3) Creating an output array by using the modified count array to output elements in sorted order.
Unit I-Data structures stack & Queue
The document discusses stacks and queues, which are common data structures that follow the Last In First Out (LIFO) and First In First Out (FIO) principles respectively. Stacks allow insertion and deletion of elements from one end only, while queues allow insertion from one end and deletion from the other end. Circular queues are better than linear queues as they make more efficient use of memory space by allowing insertion at the start when the end is reached. Multiple stacks and queues also allow managing multiple such data structures.
AVL Tree
An AVL tree is a self-balancing binary search tree that guarantees search, insertion, and deletion operations will take O(log n) time on average. It achieves this by ensuring the heights of the left and right subtrees of every node differ by at most one. When an insertion or deletion causes a height imbalance of two, rotations are performed to rebalance the tree.
Lecture 1 data structures and algorithms
This document discusses data structures and their applications. It defines key terms like data, data item, entity, attribute, field, record, and file. It explains that a data structure is a logical organization of data that specifies the data elements and operations that can be performed on them. Common operations include traversing, searching, inserting, and deleting. The choice of data structure depends on how frequently certain operations will be performed. Real-life data manipulation requires storage, retrieval, and transformation of user data.
Binary Search
This document describes binary search and provides an example of how it works. It begins with an introduction to binary search, noting that it can only be used on sorted lists and involves comparing the search key to the middle element. It then provides pseudocode for the binary search algorithm. The document analyzes the time complexity of binary search as O(log n) in the average and worst cases. It notes the advantages of binary search are its efficiency, while the disadvantage is that the list must be sorted. Applications mentioned include database searching and solving equations.
Linked List
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
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Similar to data Structure
Database
Here is a proposed design for a University database system:
Entities:
- Students
- Faculty
- Courses
- Classes
- Degrees
- Grades
Tables:
- Students (student_id, name, address, etc.)
- Faculty (faculty_id, name, department, etc.)
- Courses (course_id, name, description, etc.)
- Classes (class_id, course_id, faculty_id, semester, year)
- Degrees (degree_id, name)
- Grades (student_id, class_id, grade)
Relationships:
- Students take Classes
- Faculty teach Classes
1. Introduction to Data Structure.pptx
This document provides an introduction to data structures. It defines data as distinct pieces of information that can exist in various forms, such as numbers, text, bits and bytes. It defines data structure as a way to organize data so it can be used efficiently. Common data structures include arrays, lists, stacks, queues and files. The document outlines some important data types like integer, boolean, floating, character and string. It also discusses basic operations on data structures like traversing, searching, inserting, deleting, sorting and merging. Finally, it provides examples of different data structures and their applications.
Introduction to Data Structures
Data structures are fundamental building blocks of computer science that organize data in a computer. There are two main categories: primitive and non-primitive. Primitive structures like integers and characters are basic, while non-primitive structures like arrays and linked lists are more complex derived structures. Non-primitive structures can be linear, with elements connected sequentially, or non-linear with more complex connections. Common linear structures are stacks and queues, while common non-linear structures are trees and graphs. The choice of data structure affects the design and efficiency of algorithms and programs.
Data Structure 1..Vary Basic introduction about DSA using c++.pptx
Vary Basic introduction about DSA using c++
Introduction to data structures (ss)
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
Data structure
Data structures deal with the organization and storage of data to facilitate efficient access and modification. They allow relationships between different data elements to be expressed and enable efficient processing and accessing of data. Data structures are classified as either primitive, like integers and characters, or non-primitive, like arrays and lists. Common operations on data structures include traversing, searching, inserting, deleting, and sorting data elements.
Intro to data structures.pptx
This document discusses data structures and algorithms. It defines data structures as organized collections of data elements that allow for efficient use of data in a computer. It then covers the need for data structures, their advantages, and classifications including linear structures like arrays, linked lists, stacks and queues. The document also discusses operations on data structures like traversing, insertion, deletion, searching and sorting. It defines algorithms and provides examples of common categories like sorting, searching, deletion and insertion. Finally, it discusses analyzing algorithms based on time and space complexity.
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This document provides an overview of advanced data structures and analysis of algorithms. It discusses the need for data structures due to large amounts of data and multiple requests. Data structures provide efficiency, reusability, and abstraction. Linear data structures include arrays and linked lists, while non-linear structures include trees and graphs. Common linear data structures like stacks and queues are also described based on their insertion and deletion rules.
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This document provides an introduction to data structures. It defines key terms like data, information, records and files. It also describes different types of data structures like arrays, linked lists, stacks, queues, trees and graphs. Linear and non-linear data structures are explained. Common operations on data structures like insertion, deletion and searching are outlined. The document also defines what an algorithm is and provides an example of an algorithm to add two numbers. It concludes by describing time and space complexity analysis of algorithms.
Data structure
A data type refers to the type of values that variables hold and includes a set of values and operations. There are primitive/built-in data types that are predefined by languages like int and float, and abstract data types that separate the representation of values from operations. Common data types include boolean, char, float, double, int and string. A data structure organizes data to reduce complexity and increase efficiency of algorithms. Common linear data structures include arrays, linked lists, stacks and queues, while trees and graphs are examples of non-linear data structures.
Data structure
A data type refers to the type of values that variables hold and includes a set of values and operations. There are primitive/built-in data types that are predefined by languages like int and float, and abstract data types that separate the representation of values from operations on them. Common data types include boolean, char, float, double, int and string. A data structure organizes data to reduce complexity and increase efficiency of algorithms by controlling data flow and how data should be organized and implemented.
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Data structures provide efficient ways to store and organize data in computers. They are widely used in computer science fields like operating systems, compilers, and artificial intelligence. Data structures enhance performance by allowing fast storage and retrieval of user data. There are two main types - primitive and non-primitive. Primitive types are basic data types predefined in languages like integers while non-primitive are custom types like linked lists. Common data structures include arrays, linked lists, stacks, queues, trees, and graphs, each suited to different tasks. Understanding data structures is essential for optimizing algorithms and improving computational efficiency.
Data structures
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Apply Raw Data Set And Implement The Different Data Warngliing Functionalitie...
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Data structures
Data structures provide a way to organize and store data so that it can be used efficiently. They allow data to be processed, structured, and presented in a meaningful way to solve problems like slow searching of large datasets, limited processor speeds, and multiple simultaneous requests. Common data structures include linear structures like arrays, stacks, queues and linked lists, and non-linear structures like trees and graphs. Operations on data structures include traversing, searching, insertion, deletion, sorting, and merging.
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data Structure
- 2. § INTRODUCTION § •DATA REPRESENTATION ▪ Data . ▪ Data Type . ▪ Data Type in ‘C’ . ▪ User-defined Type . ▪ Derived Data Type . ▪ DATA STRUCTURE . ▪ NEED OF DATA STRUCTURE . ▪ CLASSIFICATION . ▪ OPERATION .
- 3. § WHAT IS DATA ? • Numbers , • Alphabets , • Symbols .
- 4. § TYPES OF DATA ? • Atomic data . • Composite data .
- 5. § ATOMIC DATA • Non-decomposible entity § COMPOSITE DATA • Composible entity
- 6. § WHAT IS DATA TYPE ? A data type is a term which refers to the kind of data that variables may hold in a programming language . Ex: int x;
- 7. § WHAT IS DATA TYPE IN ‘C’ ? • Set of built in data types. • Variables to name data. • Set of operation. • Manipulation.
- 8. § WHAT IS USER-DEFINED TYPE ? • Type definition . • Identifier . § WHAT IS DERIVED DATA TYPE ? • Group of element . • Contiguous memory .
- 9. § WHAT IS DATA STRUCTURE ? • • • • • Organization of data . Associativity among data element . Accessing method . Operation on data . Processing alternatives for data .
- 10. § NEEDS OF DATA STRUCTURE ? • • • • • • Close collection . Operations . Efficient implementation . Linked organization . Sequential organization . Modification are costly .
- 11. § CLASSIFICATION OF DATA STRUCTURE § • DATA STRUCTURE IS DIVIDED INTO TWO CATEGORIES : * Primitive data structure . * Non-primitive data structure .
- 12. § PRIMITIVE DATA STRUCTURE : • Programming languages . • Data objects . • Machine level instruction . • Examples :♦ Integers . ♦ Real . ♦ Logical data . ♦ Character data . ♦ Pointers .
- 13. § NON-PRIMITIVE DATA STRUCTURE : • Homogeneous data element . • Heterogeneous data element . • Examples :♦ Array . ♦ Structures . ♦ Union . ♦ Linked list . ♦ Stack . ♦ Queue . ♦ Tree . ♦ Graph .
- 14. § OPERATION OF DATA STRUCTURE :- I. Traversing . II. Searching . III. Inserting . IV. Deleting . V. Sorting .