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
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
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
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
Abstract: Every program whether in c, java or any other language consists of a set of commands which are based on the logic behind the program as well as the syntax of the language and does the task of either fetching or storing the data to the computer, now here comes the role of the word known as “data structure”. In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Now as different data structures are having their different usage and benefits, hence selection of the same is a task of importance. “Therefore the paper consists of the basic terms and information regarding data structures in detail later on will be followed by the practical usage of different data structures that will be helpful for the programmer for selection of a perfect data structure that would make the programme much more easy and flexible.
Keywords: Data structures, Arrays, Lists, Trees.
This is a demo presentation prepared for the recruitment of Lecturer in CSE at Green University. In this presentation, an introduction to Data Structures is demonstrated in an intuitive way.
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
Abstract: Every program whether in c, java or any other language consists of a set of commands which are based on the logic behind the program as well as the syntax of the language and does the task of either fetching or storing the data to the computer, now here comes the role of the word known as “data structure”. In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Now as different data structures are having their different usage and benefits, hence selection of the same is a task of importance. “Therefore the paper consists of the basic terms and information regarding data structures in detail later on will be followed by the practical usage of different data structures that will be helpful for the programmer for selection of a perfect data structure that would make the programme much more easy and flexible.
Keywords: Data structures, Arrays, Lists, Trees.
This is a demo presentation prepared for the recruitment of Lecturer in CSE at Green University. In this presentation, an introduction to Data Structures is demonstrated in an intuitive way.
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
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
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
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
3. Objectives: Introduction of Fundamental
Concepts
3
Overview of Data Structures
Overview of Algorithms
Some Definitions
Need of Data Structures
Advantages of Data Structures
Data Structure Classification
Operations on data structure
4. Overview of Data Structures
4
A data structure is a collection of data type ‘values’ which
are stored and organized in such a way that it allows for
efficient access and modification. In some cases a data
structure can become the underlying implementation for a
particular data.
It plays a vital role in enhancing the performance of a
software or a program as the main function of the software is
to store and retrieve the user's data as fast as possible
5. Overview of Data Structures
Data structure is representation of the logical relationship
existing between individual elements of data.
In other words, a data structure is a way of organizing all data
items that considers not only the elements stored but also their
relationship to each other.
Program = Algorithm + Data Structure
5
6. Overview of Algorithm
An algorithm is a set of instructions for solving a
problem or accomplishing a task.
Algorithms are unambiguous specifications for
performing calculation, data processing, automated
reasoning, and other tasks.
6
7. Categories Of Algorithm
The major categories of algorithms are given below:
Sort: Algorithm developed for sorting the items in certain order.
Search: Algorithm developed for searching the items inside a
data structure.
Delete: Algorithm developed for deleting the existing element
from the data structure.
Insert: Algorithm developed for inserting an item inside a data
structure.
Update: Algorithm developed for updating the existing element
inside a data structure.
7
8. Some Definitions
Database:
Refers to all data that will be dealt with in particular situation.
We’ll assume that each item in a database has a similar format.
Example: A book using index cards,
Record:
Records are the units into which a database is divided.
Ex :each card represents a record
8
9. Some Definitions
Fields:
A record is usually divided into several fields. Fields hold a
particular kind of data. Example a person’s name, address
Key:
Search for a record within a database using specific
key world. Eg search : name
9
10. Need of Data Structures
As applications are getting complexed and amount of data is
increasing day by day, there may arise the following
problems:
Processor speed: To handle very large amount 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.
10
11. Need of Data Structures
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.
Multiple requests: If thousands of users are searching the
data simultaneously on a web server, then there are the
chances that a very large server can be failed during that
process
11
12. Need of Data Structures
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.
12
13. Advantages of Data Structures
Efficiency: Efficiency of a program depends upon the choice of
data structures. For example: suppose, we have some data and
we need to perform the search for a particular record. In that
case, if we organize our data in an array, we will have to search
sequentially element by element. hence, using array may not be
very efficient here. There are better data structures which can
make the search process efficient like binary search tree or hash
tables.
13
14. Advantages of Data Structures
Reusability: Data structures are reusable, i.e. once we have
implemented a particular data structure, we can use it at any
other place.
Abstraction: Data structure is specified by the ADT which
provides a level of abstraction. The client program uses the
data structure through interface only, without getting into the
implementation details.
14
17. Types Of Data Structure
A primitive data structure is generally a basic structure
that is usually built into the language, such as an integer,
a float.
A non-primitive data structure is built out of primitive
data structures linked together in meaningful ways, such
as a or a linked-list, binary search tree, AVL Tree, graph
etc.
17
18. Types Of Data Structure
Linear Data Structures:
A data structure is called linear if all of its elements are arranged in
the linear order. In linear data structures, the elements are stored in
non-hierarchical way.
Types of Linear Data Structures are given below:
Arrays: An array is a collection of similar type of data items and
each data item is called an element of the array. The data type of the
element may be any valid data type like char, int, float or double.
18
19. Types Of Data Structure
The elements of array share the same variable name but each
one carries a different index number known as subscript. The
array can be one dimensional, two dimensional or
multidimensional.
The individual elements of the array age are:
age[0], age[1], age[2], age[3],......... age[98], age[99].
19
20. Types Of Data Structure
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.
20
21. Types Of Data Structure
Stack: Stack is a data structure in which insertion
and deletion operations are performed at one end
only.
– The insertion operation is referred to as ‘PUSH’ and
deletion operation is referred to as ‘POP’ operation.
– Stack is also called as Last in First out (LIFO) data
structure.
21
22. Types Of Data Structure
Queue: The data structure which permits the insertion at one
end and Deletion at another end, known as Queue.
– End at which deletion is occurs is known as FRONT end
and another end at which insertion occurs is known as
REAR end.
– Queue is also called as First in First out (FIFO) data
structure.
22
23. Types Of Data Structure
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.
23
24. Types Of Data Structure
Trees: Trees are multilevel data structures with a hierarchical
relationship among its elements known as nodes. The bottom most
nodes in the hierarchy are called leaf node while the top most node is
called root node. Each node contains pointers to point adjacent nodes.
Tree data structure is based on the parent-child relationship among the
nodes. Each node in the tree can have more than one children except
the leaf nodes whereas each node can have at most one parent except
the root node.
24
25. Types Of Data Structure
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.
25
26. Operations on data structure
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 .
26
27. Operations on data structure
Insertion:
Insertion can be defined as the process of adding the elements
to the data structure at any location.
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.
27
28. Operations on data structure
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 .
28
29. Operations on data structure
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.
29
30. Operations on data structure
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.
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
30