Data structures are used to organize data efficiently to perform operations on large amounts of data. They include primitive structures like integers and floats, as well as linear structures like arrays, stacks, and queues and non-linear structures like trees and graphs. Common operations on data structures include traversing, inserting, deleting, searching, and sorting data elements. Understanding which data structure to use for a given problem is important to write efficient programs as data volumes continue growing rapidly.
Which data structure is it? What are the various data structure kinds and wha...Tutort Academy
Data structures matter because they boost efficiency. Efficiency: By using the appropriate data structures, programmers can create code that runs faster and uses less memory. Reusability: By employing standard data structures, programmers can abstract the crucial operations that are carried out over numerous Data structures using libraries that are specific to Data Structures.
basics of data structure operations
Data Structures and algoithms Unit - 1.pptxmexiuro901
it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,
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
Which data structure is it? What are the various data structure kinds and wha...Tutort Academy
Data structures matter because they boost efficiency. Efficiency: By using the appropriate data structures, programmers can create code that runs faster and uses less memory. Reusability: By employing standard data structures, programmers can abstract the crucial operations that are carried out over numerous Data structures using libraries that are specific to Data Structures.
basics of data structure operations
Data Structures and algoithms Unit - 1.pptxmexiuro901
it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,
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
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose.
What are Data Structures? - Definition from WhatIs.com
TechTarget
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose.
What are Data Structures? - Definition from WhatIs.com
TechTarget
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Opendatabay - Open Data Marketplace.pptxOpendatabay
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. BUDGE BUDGE INSTITUTE OF TECHNOLOGY
INTRODUCTION OF DATA STRUCTURE
NAME – BISHAL CHOWDHURY
ROLL NO - 27600121067
YEAR - 2022
SEMESTER - 3RD
SUBJECT CODE - PCC CS-301
2. INTRODUCTION
A data structure is a group of data elements that provides the easiest way to store and perform different
actions on the data of the computer. A data structure is a particular way of organizing data in a
computer so that it can be used effectively.
Data structures are the building blocks of any program or the software. Choosing the appropriate data
structure for a program is the most difficult task for a programmer. Following terminology is used as far
as data structures are concerned.
• DATA: Data can be defined as an elementary value or the collection of values
• Record: Record can be defined as the collection of various data items
• File: A File is a collection of various records of one type of entity.
• Attribute and Entity: An entity represents the class of certain objects. it contains various attributes. Each
attribute represents the particular property of that entity.
• Field: Field is a single elementary unit of information representing the attribute of an entity.
3. NEED OF DATA STRUCTURES
• Processor speed: To handle very large amout of data, high speed processing is required, but as the data is
growing day by day to the billions of files per entity, processor may fail to deal with that much amount of data.
• Data Search: Consider an inventory size of 106 items in a store, If our application needs to search for a
particular item, it needs to traverse 106 items every time, results in slowing down the search process.
in order to solve the above problems, data structures are used. Data is organized to form a data structure in
such a way that all items are not required to be searched and required data can be searched instantly.
As applications are getting complexed and amount of data is increasing day by day, there may arrise the following
problems:
CLASSIFICATION OF DATA STRUCTURE
4. PRIMITIVE DATA STRUCTURE
Primitive data structure is a data structure that can hold a single value in a specific location whereas the non-linear
data structure can hold multiple values either in a contiguous location or random locations
The examples of primitive data structure are float, character, integer and pointer. The value to the primitive data
structure is provided by the programmer. The following are the four primitive data structures:
•Integer: The integer data type contains the numeric values. It contains the whole numbers that can be either negative
or positive. When the range of integer data type is not large enough then in that case, we can use long.
•Float: The float is a data type that can hold decimal values. When the precision of decimal value increases then the
Double data type is used.
•Character: It is a data type that can hold a single character value both uppercase and lowercase such as 'A' or 'a’.
NON-PRIMITIVE DATA STRUCTURE
The non-primitive data structure is a kind of data structure that can hold multiple values either in a contiguous or
random location. The non-primitive data types are defined by the programmer. The non-primitive data structure is
further classified into two categories, i.e., linear and non-linear data structure.
Array: An array is a data structure that can hold the elements of same type. It cannot contain the elements of different
types like integer with character. The commonly used operation in an array is insertion, deletion, traversing, searching
5. Linked List: Linked list is a linear data structure which is used to maintain a list in the memory. It can be seen
as the collection of nodes stored at non-contiguous memory locations. Each node of the list contains a pointer to
its adjacent node.
LINEAR DATA STRUCTURE:
Data structure where data elements are arranged sequentially or linearly where each and every element is
attached to its previous and next adjacent is called a linear data structure.
STACK: Stack is a linear list in which insertion and deletions are allowed only at one end, called top.
A stack is an abstract data type (ADT), can be implemented in most of the programming languages. It is named as
stack because it behaves like a real-world stack, for example: - piles of plates or deck of cards etc.
QUEUE: Queue is a linear list in which elements can be inserted only at one end called rear and deleted only at the
other end called front.
NON LINEAR DATA STRUCTURES:
This data structure does not form a sequence i.e. each item or element is connected with two or more other items in
a non-linear arrangement. The data elements are not arranged in sequential structure.
TREES: Trees are multilevel data structures with a hierarchical relationship among its elements known as nodes.
The bottommost nodes in the herierchy are called leaf node while the topmost node is called root node. Each node
contains pointers to point adjacent nodes.
Graphs: Graphs can be defined as the pictorial representation of the set of elements (represented by vertices)
connected by the links known as edges. A graph is different from tree in the sense that a graph can have cycle while
the tree can not have the one.
6. OPERATIONS ON DATA STRUCTURE:
1) TRAVERSING: Every data structure contains the set of data elements. Traversing the data structure means
visiting each element of the data structure in order to perform some specific operation like searching or sorting.
Example: If we need to calculate the average of the marks obtained by a student in 6 different subject, we need to
traverse the complete array of marks and calculate the total sum, then we will devide that sum by the number of
subjects i.e. 6, in order to find the average.
2) INSERTION: Insertion can be defined as the process of adding the elements to the data structure at any location.
If the size of data structure is n then we can only insert n-1 data elements into it.
3) DELETION: The process of removing an element from the data structure is called Deletion. We can delete an
element from the data structure at any random location.
If we try to delete an element from an empty data structure then underflow occurs.
4) SEARCHING: The process of finding the location of an element within the data structure is called Searching.
There are two algorithms to perform searching, Linear Search and Binary Search. We will discuss each one of them
later in this tutorial.
5) SORTING: The process of arranging the data structure in a specific order is known as Sorting. There are many
algorithms that can be used to perform sorting, for example, insertion sort, selection sort, bubble sort, etc.
6) MERGING: When two lists List A and List B of size M and N respectively, of similar type of elements, clubbed or
joined to produce the third list, List C of size (M+N), then this process is called merging
7. CONCLUSIONS
DATA STRUCTURE IS USEFULL IN DAY TO DAY LIFE AND ARE USING THEM
MORE FREQUENTLY THAT IS WHY IT IS SO HOT SUBJECT IN INFORMATION
TECHNOLOGY INDUSTRY.