Welcome-to-Data-Structures-and-Algorithms-Course.pptx

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Introduction to DA

Data Structures & Algorithms
(DSA)
DSA (Data Structures and Algorithms) is the study of
organizing data efficiently using data structures like
arrays, stacks, and trees, paired with step-by-step
procedures (or algorithms) to solve problems effectively.

What is a Data Structure?
A data structure is a way of organizing and storing data. It's designed
for efficient access and modification. Think of it like a well-organized
library, not a pile of books.
Organization
Efficiently arrange your data
Storage
Maximize disk usage for your
data
Relationships
Understand your data connections

Common Data Structures
Let's preview some common data structures. Each of these are tools to solve different problems.
Arrays
Ordered collections with
direct access via index.
Linked Lists
Sequences of nodes with
flexible size.
Trees
Hierarchical structures for
searching and sorting.
Hash Tables
Key-value pairs for fast
lookups.
Why to Learn DSA?
• Learning DSA boosts your problem-solving abilities and make you a stronger programmer.
• DSA is foundation for almost every software like GPS, Search Engines, Databases, Web Applications, etc.
• Top Companies like Google, Microsoft, Amazon, Apple, Meta and many other heavily focus on DSA in interviews.

What is an Algorithm?
An algorithm is a step-by-step procedure for solving a problem. They
have well-defined inputs, outputs, and finite steps. Efficiency is key,
using time and space complexity.
1 Defined
Inputs/Outputs
Every algorithm needs clear
parameters.
2 Finite Steps
Algorithms must have a
limited number of steps.
3 Effective
Achieve desired result.

Why Data Structures and
Algorithms?
We need data structures and algorithms to write efficient, scalable code. Real-
world example applications include Google Maps, Facebook, and databases.
Efficient Code
Effective Solutions
Foundation of Software

Data Structures + Algorithms = Programs
Algorithms operate on data structures, impacting efficiency. Optimizing this combination is critical for best performance. Choosing
the right data structure matters!
Data Structures 1
Algorithms
2
Programs
3

Introduction to Big O Notation
Big O notation measures algorithm efficiency, like time and space complexity. Understand the impact of complexities such as
O(1), O(log n), O(n), and O(n^2) on performance.
1
O(1)
Excellent
2 O(log n)
Good
3
O(n)
Fair
4 O(n^2)
Poor

OOP Revision: Key Concepts
OOP concepts include encapsulation, inheritance, and polymorphism. These concepts enhance code organization and
reusability. Encapsulation protects data within a class.
Encapsulation
Protect your data.
Inheritance
Reuse existing code.
Polymorphism
Flexibility in forms.
Abstraction
No need to understand
complex logic

OOP and Data Structures
OOP provides a framework for organizing code and data. Data structures can be implemented as classes in OOP. Design
patterns use OOP to construct robust software.
1 Organize Code
2 Implement Classes
3 Construct Software

Next Steps
Upcoming topics include arrays, linked lists, trees, graphs, sorting, and
searching. Practice and problem-solving are essential in this course.
Practice
Essential to master DSA
Resources
Explore books and courses
Problem Solving
Apply your knowledge.

This document provides an overview of a course on data structures and algorithms. The course covers fundamental data structures like arrays, stacks, queues, lists, trees, hashing, and graphs. It emphasizes good programming practices like modularity, documentation and readability. Key concepts covered include data types, abstract data types, algorithms, selecting appropriate data structures based on efficiency requirements, and the goals of learning commonly used structures and analyzing structure costs and benefits.

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1. Data Structures & Algorithms (DSA) DSA (Data Structures and Algorithms) is the study of organizing data efficiently using data structures like arrays, stacks, and trees, paired with step-by-step procedures (or algorithms) to solve problems effectively.

2. What is a Data Structure? A data structure is a way of organizing and storing data. It's designed for efficient access and modification. Think of it like a well-organized library, not a pile of books. Organization Efficiently arrange your data Storage Maximize disk usage for your data Relationships Understand your data connections

3. Common Data Structures Let's preview some common data structures. Each of these are tools to solve different problems. Arrays Ordered collections with direct access via index. Linked Lists Sequences of nodes with flexible size. Trees Hierarchical structures for searching and sorting. Hash Tables Key-value pairs for fast lookups. Why to Learn DSA? • Learning DSA boosts your problem-solving abilities and make you a stronger programmer. • DSA is foundation for almost every software like GPS, Search Engines, Databases, Web Applications, etc. • Top Companies like Google, Microsoft, Amazon, Apple, Meta and many other heavily focus on DSA in interviews.

4. What is an Algorithm? An algorithm is a step-by-step procedure for solving a problem. They have well-defined inputs, outputs, and finite steps. Efficiency is key, using time and space complexity. 1 Defined Inputs/Outputs Every algorithm needs clear parameters. 2 Finite Steps Algorithms must have a limited number of steps. 3 Effective Achieve desired result.

5. Why Data Structures and Algorithms? We need data structures and algorithms to write efficient, scalable code. Real- world example applications include Google Maps, Facebook, and databases. Efficient Code Effective Solutions Foundation of Software

6. Data Structures + Algorithms = Programs Algorithms operate on data structures, impacting efficiency. Optimizing this combination is critical for best performance. Choosing the right data structure matters! Data Structures 1 Algorithms 2 Programs 3

7. Introduction to Big O Notation Big O notation measures algorithm efficiency, like time and space complexity. Understand the impact of complexities such as O(1), O(log n), O(n), and O(n^2) on performance. 1 O(1) Excellent 2 O(log n) Good 3 O(n) Fair 4 O(n^2) Poor

8. OOP Revision: Key Concepts OOP concepts include encapsulation, inheritance, and polymorphism. These concepts enhance code organization and reusability. Encapsulation protects data within a class. Encapsulation Protect your data. Inheritance Reuse existing code. Polymorphism Flexibility in forms. Abstraction No need to understand complex logic

9. OOP and Data Structures OOP provides a framework for organizing code and data. Data structures can be implemented as classes in OOP. Design patterns use OOP to construct robust software. 1 Organize Code 2 Implement Classes 3 Construct Software

10. Next Steps Upcoming topics include arrays, linked lists, trees, graphs, sorting, and searching. Practice and problem-solving are essential in this course. Practice Essential to master DSA Resources Explore books and courses Problem Solving Apply your knowledge.

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