This document provides an overview and introduction to data structures. It discusses key terminology like data, data items, and fields. It also covers different types of data structures like linear (arrays, linked lists) and non-linear (trees, graphs) structures. Common data structure operations like traversing, searching, inserting and deleting are explained. The document stresses the importance of selecting the appropriate data structure based on the problem and required operations. It also briefly discusses algorithm design, implementation, testing, and analysis of time and space complexity.
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Programs are complete in best of my knowledge with zero compilation error in IDE Bloodshed Dev-C++. These can be easily portable to any versions of Visual Studio or Qt. If you need any guidance please let me know via comments and Always Enjoy Programming.
Stack and its Applications : Data Structures ADTSoumen Santra
Stack is an Abstract Data Type (ADT), Last in First out (LIFO) concept.
Features of Stack: Abstract Data Type (ADT)
C implementations of Stack's Functions like push(), pop(), isEmpty(), isOverflow(), peep()
Stack Applications like Tower of Hanoi, Infix to Postfix Conversion, Postfix Evaluation, Parenthesis Checking etc.
Diagramatic Representation of Tower of Hanoi, Infix to Postfix Conversion & Postfix Evaluation
This is a presentation on Arrays, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
This is a seminar presentation on "SORTING" for Semester 2 exam at St. Xavier's College.The power point presenation deals with the requirement of sorting in our life,types of sorting techniques,code for implementing them,the time and space complexity of different sorting algorithms,the applications of sorting,its use in the industry and its future scope.The slide show contains .gif files which can't be seen here.For more details or any queries send me a mail at agmajumder@gmail.com
Programs are complete in best of my knowledge with zero compilation error in IDE Bloodshed Dev-C++. These can be easily portable to any versions of Visual Studio or Qt. If you need any guidance please let me know via comments and Always Enjoy Programming.
Stack and its Applications : Data Structures ADTSoumen Santra
Stack is an Abstract Data Type (ADT), Last in First out (LIFO) concept.
Features of Stack: Abstract Data Type (ADT)
C implementations of Stack's Functions like push(), pop(), isEmpty(), isOverflow(), peep()
Stack Applications like Tower of Hanoi, Infix to Postfix Conversion, Postfix Evaluation, Parenthesis Checking etc.
Diagramatic Representation of Tower of Hanoi, Infix to Postfix Conversion & Postfix Evaluation
This is a presentation on Arrays, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
This is a seminar presentation on "SORTING" for Semester 2 exam at St. Xavier's College.The power point presenation deals with the requirement of sorting in our life,types of sorting techniques,code for implementing them,the time and space complexity of different sorting algorithms,the applications of sorting,its use in the industry and its future scope.The slide show contains .gif files which can't be seen here.For more details or any queries send me a mail at agmajumder@gmail.com
Database management system full theory portion is covered. It's helpful to students who are in any management courses.all the best to all of you, this ppt might be helpful for you.Database management system full theory portion is covered. It's helpful to students who are in any management courses.all the best to all of you, this ppt might be helpful for you.Database management system full theory portion is covered. It's helpful to students who are in any management courses.all the best to all of you, this ppt might be helpful for you.Database management system full theory portion is covered. It's helpful to students who are in any management courses.all the best to all of you, this ppt might be helpful for you.
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.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
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
2. Recommended books
Data Structures By Seymour Lipschutz
[Schaum’s Outline]
An Introduction to Data structures with
Applications by Tremblay and Sorenson
3. LECTURE 1 ConTEnTS:-
Overview
Basic Terminology
Introduction to Data Structure
Data Structure Types
Data Structure Operations
Selecting a data structure
4. ovERviEw:-
The study of computer science teaches us how to
use computers and how to organize the data so
that they can be manipulated by a program.
The term data structure refers to a scheme for
organizing data into memory.
Organization of data in some cases is of immense
importance. Therefore, the data will be stored in a
special way so that the required result should be
calculated as fast as possible.
5. BASiC TERminoLogy:
DATA: Data are simply values or set of values. Or
data is raw material which we fed in computer for
processing.
DATA iTEmS: A data item refers to a single unit of
values.
gRoUp iTEmS: Data items that are divided into
sub items are called group items.
e.g an employee’s name may divide into three
sub items, first name, middle name, and last
name.
infoRmATion: Meaningful or processed data.
EnTiTy: An entity is something that has certain
attributes or properties which may assigned
values.
6. In data structure collection of data is frequently
organized in to hierarchy of fields, records and
files.
fiELD: a field is an single elementary unit of
information representing an attribute of an entity.
RECoRD: A record is a collection of field values of a
given entity.
fiLE: A file is a collection of records of the entities
in a given entity set.
Attributes Name Age Address NIC No
Values
Ali 24 Hyd 41303123
Azam 22 Khi 41303254
Adnan 21 Lahore 41312549
Field
Record
File
7. IntroductIon to data
Structure:
data Structure: A data structure is
specialized format for organizing and storing data.
or
In computer science, a data Structure is a
way of storing data in a computer memory so that
it can be used efficiently.
8. Importance of Data Structure
Let’s discuss why we need data structures and what sort of
problems can be solved with their use. Data structures help us to
organize the data in the computer, resulting in more efficient
programs.
An efficient program executes faster and helps minimize the
usage of resources like memory, disk.
Computers are getting more powerful with the passage of time
with the increase in CPU speed in GHz, availability of faster
network and the maximization of disk space. Therefore people
have started solving more and more complex problems.
As computer applications are becoming complex, so there is
need for more resources. This does not mean that we should buy
a new computer to make the application execute faster. Our
effort should be to ensue that the solution is achieved with the
help of programming, data structures and algorithm.
9. What does organizing the data
mean?
It means that the data should be arranged in a way that it
is easily accessible.
Because data is inside the computer and we want to see it.
We may also perform some calculations on it.
Suppose the data contains some numbers and the
programmer wants to calculate the average, standard
deviation etc. May be we have a list of names and want to
search a particular name in it. To solve such problems,
data structures and algorithm are used.
Sometimes you may realize that the application is too slow
and taking more time. There are chances that it may be
due to the data structure used, not due to the CPU speed
and memory.
10. data Structure typeS:-
Data structure are classified either Linear or non-
linear.
LInear data Structure: A data structure is linear if every
item is related (or attached) to its pervious and next item (e.g
Array, Linked list)
non-LInear data Structure: A data structure is non-linear if
every item is attached to many other items in specific ways to
reflect relationships (e.g Trees)
12. data Structure
operatIonS:-
The data appearing in our data structure is
processed by means of certain operations.
The following four operations play a major role:
Transversing
Searching
Inserting
Deleting
13. Transversing: Accessing each record exactly once so that
certain items in the record may be processed.
This accessing or processing is sometimes called ‘visiting’ the
records.
searching: finding the location of the record or finding the
location of all records, which satisfy one or more conditions.
inserTing: Adding new records to the structure.
DeleTing: Removing a record from the structure.
Sometimes two or more operations may be used in a given
situation; e.g we may want to delete the record which may
mean we first need to search for record and then delete it from
structure.
14. DaTa sTrucTure operaTions conT…
The following two operations which are used in
special situations will also be considered.
sorTing: Arranging the records in some logical
orders.
Merging: Combining the records in two
different sorted files into a single file.
15. selecTing a DaTa sTrucTure:-
How we can select the data structure?
There are different kinds of data structure suited to different
kinds of applications and some are highly specialized to
certain tasks.
Whenever we need to select a data structure we must keep
some points in mind.
Select the data structure as follows:
First of all, we have to analyze the problem to
determine the resources constraints that a
solution must meet.
Secondly, it is necessary to determine the basic
operations that must be supported. Quantify
the resources constraints for each operations.
Finally, select the data structure that meets
these requirements the maximum.
16. Algorithm Design/Specifications
Algorithm: Finite set of instructions that, if followed,
accomplishes a particular task.
Describe: in natural language / pseudo-code /
diagrams / etc.
Criteria to follow:
Input: Zero or more quantities (externally produced)
Output: One or more quantities
Definiteness: Clarity, precision of each instruction
Effectiveness: Each instruction has to be basic
enough and feasible
Finiteness: The algorithm has to stop after a finite
(may be very large) number of steps
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17. Implementation, Testing and Maintenance
Implementation
Decide on the programming language to use
C, C++, Python, Java, Perl, etc.
Write clean, well documented code
Test, test, test
Integrate feedback from users, fix bugs,
ensure compatibility across different
versions Maintenance 17
18. Algorithm Analysis
Space complexity
How much space is required
Time complexity
How much time does it take to run the
algorithm
18
19. Space Complexity
Space complexity = The amount of memory
required by an algorithm to run to completion
the most often encountered cause is “memory
leaks” – the amount of memory required larger
than the memory available on a given system
Some algorithms may be more efficient if data
completely loaded into memory
Need to look also at system limitations
e.g. Classify 2GB of text in various categories –
can I afford to load the entire collection?
19
20. Space Complexity (cont…)
1. Fixed part: The size required to store certain
data/variables, that is independent of the size of the
problem:
- e.g. name of the data collection
1. Variable part: Space needed by variables, whose
size is dependent on the size of the problem:
- e.g. actual text
- load 2GB of text VS. load 1MB of text
20
21. Time Complexity
Often more important than space complexity
space available tends to be larger and larger
time is still a problem for all of us
3-4GHz processors on the market
still …
researchers estimate that the computation of
various transformations for 1 single DNA chain for
one single protein on 1 TerraHZ computer would
take about 1 year to run to completion
Algorithms running time is an important issue
21