The document introduces different data structures commonly used in programming, including lists, arrays, stacks, queues, trees, and graphs. It provides examples of how queues and binary trees are structured and when each would be an appropriate data structure to use. Programmers must choose which data structure to implement based on the nature of the data and the necessary operations. Alice supports lists and arrays as basic built-in data structures that can be used to represent other data structures or organize program data.
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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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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Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
this presentation is made for the students who finds data structures a complex subject
this will help students to grab the various topics of data structures with simple presentation techniques
best regards
BCA group
(pooja,shaifali,richa,trishla,rani,pallavi,shivani)
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It is a presentation on some Searching and Sorting Techniques for Computer Science.
It consists of the following techniques:
Sequential Search
Binary Search
Selection Sort
Bubble Sort
Insertion Sort
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 Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
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Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
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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:
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Benefits of a Precise Ecosystem:
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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.
The affect of service quality and online reviews on customer loyalty in the E...
Introduction of Data Structure
1. Introduction to Data Structures
CREATED IN:- MANDAVI CLASSES
CREATED BY :-KRISHNA CHANDRA
2. Data Structures
A data structure is a scheme
for organizing data in the
memory of a computer.
Some of the more commonly
used data structures include
lists, arrays, stacks, queues,
heaps, trees, and graphs.
Binary Tree
3. Data Structures
The way in which the data is
organized affects the
performance of a program
for different tasks.
Computer programmers
decide which data structures
to use based on the nature
of the data and the
processes that need to be
performed on that data.
Binary Tree
4. Example: A Queue
A queue is an example of commonly used simple data
structure. A queue has beginning and end, called the
front and back of the queue.
Data enters the queue at one end and leaves at the other.
Because of this, data exits the queue in the same order in
which it enters the queue, like people in a checkout line at
a supermarket.
5. Example: A Binary Tree
A binary tree is another
commonly used data
structure. It is organized like
an upside down tree.
Each spot on the tree, called
a node, holds an item of data
along with a left pointer and
a right pointer. Binary Tree
6. Example: A Binary Tree
The pointers are lined up
so that the structure forms
the upside down tree, with
a single node at the top,
called the root node, and
branches increasing on the
left and right as you go
down the tree.
Binary Tree
7. Choosing Data Structures
By comparing the queue with
the binary tree, you can see
how the structure of the data
affects what can be done
efficiently with the data.
8. Choosing Data Structures
A queue is a good data structure
to use for storing things that need
to be kept in order, such as a set
of documents waiting to be
printed on a network printer.
.
9. Choosing Data Structures
The jobs will be printed in the
order in which they are received.
Most network print servers
maintain such a print queue.
.
10. Choosing Data Structures
A binary tree is a good data
structure to use for searching
sorted data.
The middle item from the list is
stored in the root node, with
lesser items to the left and greater
items to the right.
11. Choosing Data Structures
A search begins at the root. The
computer either find the data, or
moves left or right, depending on
the value for which you are
searching.
Each move down the tree cuts the
remaining data in half.
12. Choosing Data Structures
Items can be located very quickly
in a tree.
Telephone directory assistance
information is stored in a tree, so
that a name and phone number
can be found quickly.
13. Choosing Data Structures
For some applications, a queue is
the best data structure to use.
For others, a binary tree is better.
Programmers choose from
among many data structures
based on how the data will be
used by the program.
14. Data Structures in Alice
Alice has two built-in data structures
that can be used to organize data, or
to create other data structures:
• Lists
• Arrays
15. Lists
A list is an ordered set of data. It is often used to store
objects that are to be processed sequentially.
A list can be used
to create a queue.
16. Arrays
An array is an indexed set of variables, such as
dancer[1], dancer[2], dancer[3],… It is like a set of
boxes that hold things.
A list is a set of items.
An array is a set of
variables that each
store an item.
17. Arrays and Lists
You can see the difference between arrays and
lists when you delete items.
18. Arrays and Lists
In a list, the missing spot is filled in when
something is deleted.
19. Arrays and Lists
In an array, an empty variable is left behind
when something is deleted.
20. Lists
A list is created in Alice by checking the make a
list box when creating a new variable.
Make a list box
21. Lists
The For all in order and For all together tiles can
be used to work with lists. They are at the
bottom of the editor area.
22. Arrays
Arrays can be created in a similar manner, but
more often they are created using the array
visualization object from the Alice local gallery.
The Array Visualization object
has special properties and
methods for manipulating
the elements in an array.
23. Arrays
Alice has a set of built-in functions that can be
performed on arrays.