This document provides an introduction to data structures and summarizes key concepts. It defines data structures as organized ways to store and access data to enable efficient operations. Common data structures include arrays, stacks, queues, linked lists, trees, and graphs. The document explains that data structures are important for efficient data access, optimal memory usage, algorithm design, and problem solving. It also distinguishes data structures from databases before summarizing common types of data structures.

1. CS222
Lecture 1: Introduction

2. Objectives
To define data structures and explain their importance
To differentiate between data structures and databases
 To summarize the concepts of :
 Arrays and Multidimension Arrays
 Stack
Queue
Linked List
Tree
Graph

3. Introduction
“ Get your data structures correct first, and
the rest of the program will write itself.”
- David Jones
Program = Data Structures + Algorithms

4. Definition
 A data structure is a way of organizing and storing data in a computer
system or memory to enable efficient operations, such as data retrieval,
insertion, deletion, and manipulation.
It provides a logical and systematic arrangement of data elements, along
with relationships and operations defined on those elements.
Data structures serve as the foundation for designing algorithms and solving
problems in various domains of computer science and programming.

5. Importance
Efficient Data Organization: Data structures provide efficient organization
and storage mechanisms for managing and manipulating large volumes of
data.
 Fast Data Access: Well-designed data structures enable quick and direct
access to specific data elements, enhancing performance and reducing
retrieval time.
Optimal Memory Utilization: Data structures help in optimizing memory
usage by minimizing memory overhead and providing efficient allocation and
deallocation of resources.

6. Importance
Algorithm Design: Data structures are the building blocks for designing
efficient algorithms and solving complex computational problems.
Code Maintainability: Properly designed data structures enhance code
maintainability by providing clear and organized ways to manage and
access data, making it easier for developers to understand and modify code.
Search and Retrieval Efficiency: Certain data structures, such as binary
search trees and hash tables, offer efficient search and retrieval operations,
reducing time complexity.

7. Importance
Sorting and Sorting Algorithms: Data structures facilitate sorting
operations by providing efficient algorithms like quicksort, merge sort, and
heapsort.
 Code Reusability: By implementing common data structures, developers
can reuse code and leverage existing libraries, saving time and effort in
application development.
Scalability and Performance: Properly chosen data structures contribute
to scalable and high-performance systems, handling increasing data loads
efficiently.

8. Importance
Problem Solving and Software Engineering: A solid understanding of
data structures is crucial for effective problem-solving and designing robust
software applications.
 Dynamic Data Handling: Dynamic data structures, such as dynamic
arrays and linked lists, adapt to changing data sizes, providing flexibility and
efficient memory usage.
Data Compression: Certain data structures, such as Huffman trees,
facilitate data compression techniques, reducing storage space and
improving data transmission.

9. Data Structures vs. Databases

10. Types of Data Struct
1. Array 2. Linked List 3. Stack 4. Queue
5. Tree 6. Graph 7. Hash Table 8. Heap
9. Trie 10. Matrix 11. Set 12. Dictionary
13. Priority Queue 14. Disjoint Set 15. Bloom Filter 16. Red-Black Tree
17. B-tree 18. Sparse Matrix 19. Circular Buffer 20. Graph Adjacency List

11. Array
Short Description: An array is a contiguous block of memory that stores a
fixed-size sequence of elements of the same type.
Sample Applications:
 Sorting Algorithms
 Search Algorithms
 Counting and Frequency Analysis

12. Multidimensional Array
Short Description: A multidimensional array is an array with multiple
dimensions, such as 2D or 3D arrays.
Sample Applications:
 Storing and manipulating images or matrices in image processing and
scientific computations.
 Implementing game boards or grids for games and simulations.
 Representing tabular data in a spreadsheet or database.

13. Stack
Short Description: A stack is a Last-In-First-Out (LIFO) data structure that
stores elements in a sequential manner, allowing insertion and deletion only
at one end.
Sample Applications:
 Function call stack in programming languages for managing program
execution and memory allocation.
 Undo/Redo functionality in text editors or graphic design software.
 Depth-first search algorithm implementation in graph traversal.

14. Queue
Short Description: A queue is a First-In-First-Out (FIFO) data structure that
stores elements in a sequential manner, allowing insertion at one end and
deletion at the other end.
Sample Applications:
 Print spooling, where print jobs are queued and processed in the order
they are submitted.
 Process scheduling in operating systems
 Message queuing systems for reliable message delivery in distributed
systems.

15. Linked List
Short Description: A linked list is a linear data structure consisting of nodes
that contain data and a reference to the next node, forming a sequence.
Sample Applications:
 Implementing a stack or queue using a linked list as the underlying data
structure.
 Managing dynamic data structures like memory allocation in operating
systems.
 Implementing sparse matrices or sparse data structures to optimize
memory usage.

16. Tree
Short Description: A tree is a hierarchical data structure composed of
nodes connected by edges, with a root node at the top and child nodes
branching out from it.
Sample Applications:
 Representing file systems, where directories and subdirectories are
organized hierarchically.
 Implementing binary search trees for efficient searching and sorting.
 Parsing and evaluating mathematical expressions using expression
trees.

17. Graph
Short Description: A graph is a collection of vertices (nodes) connected by
edges, representing relationships or connections between entities.
Sample Applications:
 Social network analysis, where individuals and their connections are
represented by vertices and edges.
 Routing algorithms in transportation networks or computer networks.
 Image recognition and object detection using graph-based models like
convolutional neural networks.

18. CS222
End of Lecture 1
Next Lecture: Arrays