Discover what data structures and algorithms are, why they're fundamental to computer science, and understand the key distinctions between data and information that every programmer should know.

Learning Objectives

Problem Solving

Systematic approaches to computational problems

Core Concepts

Fundamental building blocks of computer science

Best Practices

Industry-standard development approaches

Why Study Data Structures and Algorithms?

Efficient Solutions

Apply best practices for developing optimal solutions using programming

Right Tool for Job

Understand concepts to apply the best-fit data structure for requirements

System Architecture

Design, develop and optimize applications using computer programming

Reliable Solutions

Develop effective and reliable software solutions

Understanding Data and Information

Input → Processing → Output

Raw Data

Processing

Information

Data

Raw facts and figures without context or meaning

42"John Smith"2024-10-06

Information

Processed data that has meaning and context

Age: 42 years

Customer: John Smith

Today's Date

Types of Data: Atomic vs Composite

Atomic Data

Single, indivisible value that cannot be broken down further

integer: 123float: 45.67char: 'A'boolean: true

Composite Data

Can be broken into subfields or contains multiple values

Student {roll_no: 123name: "John"faculty: "CS"}

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