The document provides an introduction to data structures and algorithms, defining data structures as organized formats for data storage that enhance efficiency and reduce operational complexity. It discusses essential characteristics of data structures, their types (linear and non-linear), and the necessity of algorithms for problem-solving, emphasizing their qualities and the common issues faced in data handling. Real-world applications are also illustrated, demonstrating the practical use of data structures in scenarios such as supermarkets and libraries.

1. INTRO TO
Data Structures
&
Algorithms

2. Introduction
Data Structure
• Data Structure is a way of collecting and organizing data in such
a way that we can perform operations on these data in an
effective way. Data Structures is about rendering data elements
in terms of some relationship, for better organization and
storage.
• Data Structures are structures programmed to store ordered
data, so that various operations can be performed on it easily. It
represents the knowledge of data to be organized in memory.
• It should be designed and implemented in such a way that it
reduces the complexity and increases the efficiency.arial
D A TA S T R U C T U R E S &

3. 3
Characteristics of a Data Structure
• Correctness − Data structure implementation should implement
its interface correctly.
• Time Complexity − Running time or the execution time of
operations of data structure must be as small as possible.
• Space Complexity − Memory usage of a data structure operation
should be as little as possible.
D A TA S T R U C T U R E S &

4. 4
Data
Is a collection of numbers, symbol or characters.
It is raw and organized.
D A TA S T R U C T U R E S &

5. 5
Information
An organized collection of data, that is arranged meaningfully.
D A TA S T R U C T U R E S &

6. 6
Data Structures
• Is a data organization, management and storage format
that enables efficient access and modification.
• Is a way in which a Data is stored on a Device.
D A TA S T R U C T U R E S &

7. 7
Why Do We Need Data Structures?
So that we can Store Data in a particular situation efficiently
and use our resources in the most productive way possible.
D A TA S T R U C T U R E S &

8. 8
Real World Application
Supermarket
• Stored Categorically
• Efficient Searching
D A TA S T R U C T U R E S &

9. 9
Real World Application
Library
• Stored Alphabetically
• Efficient Searching
D A TA S T R U C T U R E S &

10. 10
Real World Application
Library
• Stored as Coordinates
• Efficient Searching
D A TA S T R U C T U R E S &

11. 11
Variables
D A TA S T R U C T U R E S &
A Temporary storage for data which is stored in our RAM.

12. 12
Array / Collections
D A TA S T R U C T U R E S &
• A Data Structure that contains a group of values / variables
that can be accessed by using an index.
• In most cases their values are of the Same Datatype.
• They are commonly used to organize data so that it can be
easily sorted or searched.

13. 13
Programming Application
D A TA S T R U C T U R E S &
Array in C++ WITHOUT SORTING.
Assume that it takes 1 second for each element to be read

14. 14
Programming Application
D A TA S T R U C T U R E S &
Array in C++ WITHOUT SORTING.
Let’s search for “RM-101” by iterating the array.

15. 15
Programming Application
D A TA S T R U C T U R E S &
Array in C++ WITHOUT SORTING.
Searching for “RM-101”
1s 2s 3s
Found “RM-101” after 3 seconds.

16. 16
Programming Application
D A TA S T R U C T U R E S &
Array in C++ WITH SORTING.
Let’s search for “RM-101” by iterating the array.
1s
Found “RM-101” after 1 second

17. 17
Programming Application
D A TA S T R U C T U R E S &
Array in C++ WITH SORTING.
Let’s search for “RM-103” by iterating the array from the end.
1s
Found “RM-103” after 2 second
2s
Searching for “RM-103”

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Need for Data Structure
As applications are getting complex and data rich, there are three common problems that applications face
now-a-days.
• Data Search − Consider an inventory of 1 million (106) items of a store. If the application is to
search an item, it has to search an item in 1 million (106) items every time slowing down the
search. As data grows, search will become slower.
• Processor Speed − Processor speed although being very high, falls limited if the data grows to
billion records.
• Multiple Requests − as thousands of users can search data simultaneously on a web server, even
the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized in a data
structure in such a way that all items may not be required to be searched, and the required data can be
searched almost instantly

19. 19
What is an Algorithm?
• Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a
certain order to get the desired output. Algorithms are generally created independent of
underlying languages, i.e. an algorithm can be implemented in more than one programming
language.
• In computer programming terms, an algorithm is a set of well-defined instructions to solve a
particular problem. It takes a set of input(s) and produces the desired output.
For example:
An algorithm to add two numbers:
• Take two number inputs
• Add numbers using the + operator
• Display the result

20. 20
Let us consider the problem of preparing an omelette. To prepare an omelette, we follow the steps
given below:
1) Get the frying pan.
2) Get the oil.
a. Do we have oil?
• If yes, put it in the pan.
• If no, do we want to buy oil?
 If yes, then go out and buy.
 If no, we can terminate.
3)Turn on the stove, etc...
What we are doing is, for a given problem (preparing an omelette), we are providing a step-by
step procedure for solving it. The formal definition of an algorithm can be stated as: An algorithm
is the step-by-step unambiguous instructions to solve a given problem.

21. 21
Qualities of a Good Algorithm
• Input and output should be defined precisely.
• Each step in the algorithm should be clear and unambiguous.
• Algorithms should be most effective among many ways to solve a problem.
• An algorithm shouldn't include computer code. Instead, the algorithm should be written in
such a way that it can be used in different programming languages.

22. 22
From the data structure point of view, following are some important categories of algorithms −
• Search − Algorithm to search an item in a data structure.
• Sort − Algorithm to sort items in a certain order.
• Insert − Algorithm to insert item in a data structure.
• Update − Algorithm to update an existing item in a data structure.
• Delete − Algorithm to delete an existing item from a data structure.

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Characteristics of an Algorithm
Not all procedures can be called an algorithm. An algorithm should have the following
characteristics −
• Unambiguous − Algorithm should be clear and unambiguous. Each of its steps (or
phases), and their inputs/outputs should be clear and must lead to only one meaning.
• Input − an algorithm should have 0 or more well-defined inputs.
• Output − an algorithm should have 1 or more well-defined outputs, and should match
the desired output.
• Finiteness − Algorithms must terminate after a finite number of steps.
• Feasibility − should be feasible with the available resources.
• Independent − an algorithm should have step-by-step directions, which should be
independent of any programming code.

24. 24
Algorithm 1: Add two numbers entered by the user
Step 1: Start
Step 2: Declare variables num1, num2 and sum.
Step 3: Read values num1 and num2.
Step 4: Add num1 and num2 and assign the result to sum.
sum←num1+num2
Step 5: Display sum
Step 6: Stop

25. 25
Algorithm 2: Find the largest number among three numbers
Step 1: Start
Step 2: Declare variables a,b and c.
Step 3: Read variables a,b and c.
Step 4: If a > b
If a > c
Display a is the largest number.
Else
Display c is the largest number.
Else
If b > c
Display b is the largest number.
Else
Display c is the greatest number.
Step 5: Stop

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A good algorithm maintains a level of correctness while being efficient.
Meaning, there is little error, and it doesn’t take much time to complete.
Another important component is comprehensibility. We wouldn’t be able
to use algorithms so frequently if they couldn’t be understood.
Algorithmic and computational thinking is so pervasive that it governs the
simplest things in our daily lives. Here are some examples of algorithms
you interact with everyday.

27. 27
Recipes
Just like sorting papers and even tying your shoes, following a recipe is a type of
algorithm. The goal of course being to create a duplicated outcome. In order to
complete a recipe, you must follow a given set of steps. Say you are making bread.
You need flour, yeast and water. After you have your ingredients, you need to combine
them in a certain way that will create a predictable outcome, in this case a loaf of
bread.

28. 28
A simple task and yet it uses algorithmic thinking. When you are sorting
office files or your personal documents you are implementing an
algorithm. In its most basic sense, you are following a set of tasks to
achieve an outcome. The reason why sorting papers is a great example, is
because it shows the variety of tasks and specifications algorithms can
use. For instance, you can sort your files alphabetically, by word count, by
date, and countless others. The goal is to simplify the organizational
process by using small tasks.
Sorting Papers

29. 29
ACTIVITY TIME!

30. 30
Write down at least 3 algorithms
you interact with everyday and
present it in front of the class.

31. 31
Types of Data Structure
Basically, data structures are divided into two categories:
• Linear data structure
• Non-linear data structure
Linear data structures
• In linear data structures, the elements are arranged in sequence one after the other. Since
elements are arranged order, they are easy to implement.
• However, when the complexity of the program increases, the linear data structures might
not be the best choice because of operational complexities

32. 32
Popular linear data structures are:
1. Array Data Structure
In an array, elements in memory are arranged in continuous memory. All the elements of an array
are of the same type. And the type of elements that can be stored in the form of arrays is determined
by the programming language.
An array with each element represented by an index

33. 33
2. Stack Data Structure
In stack data structure, elements are stored in the LIFO principle. That is, the last element stored in
a stack will be removed first.
It works just like a pile of plates where the last plate kept on the pile will be removed first.
In a stack, operations can be performed only from one end (top here).

34. 34
3. Queue Data Structure
Unlike stack, the queue data structure works in the FIFO principle where first element stored in the
queue will be removed first.
It works just like a queue of people in the ticket counter where first person on the queue will get the
ticket first.
In a queue, addition and removal are performed from separate ends.

35. 35
4. Linked List Data Structure
In linked list data structure, data elements are connected through a series of nodes. And each node
contains the data items and address to the next node.
A linked list

36. 36
Unlike linear data structures, elements in non-linear data structures are not in any sequence. Instead they
are arranged in a hierarchical manner where one element will be connected to one or more elements.
Non-linear data structures are further divided into graph and tree-based data structures.
1. Graph Data Structure
In graph data structure, each node is called vertex and each vertex is connected to other
vertices through edges.
Graph data structure example
Non-linear data structures

37. 37
2. Trees Data Structure
Like a graph, a tree is also a collection of vertices and edges. However, in tree data
structure, here can only be one edge between two vertices.
Tree data structure example
Non-linear data structures

38. 38
Now that we know about linear and non-linear data structures, let's see the major differences between
them.
Linear Vs Non-linear Data Structures
Linear Data Structures Non-Linear Data Structures
The data items are arranged in sequential
order, one after the other.
The data items are arranged in non-sequential
order (hierarchical manner).
All the items are present on the single layer. The data items are present at different layers.
It can be traversed on a single run. That is, if we
start from the first element, we can traverse all
the elements sequentially in a single pass.
It requires multiple runs. That is, if we start from
the first element it might not be possible to
traverse all the elements in a single pass.
The memory utilization is not efficient.
Different structures utilize memory in different
efficient ways depending on the need.
The time complexity increases with the data
size.
Time complexity remains the same.
Example: Arrays, Stack, Queue Example: Tree, Graph, Map

39. Thank you
Presenter name: Ellen Grace D. Porras
Email address: egporras@psu.palawan.edu.ph