Data Structures and Algorithms

Importance of Data Structures

• Common interview topic for tech companies.
• Foundation of software industry: handling and processing data.
• Efficient data storage is crucial for performance and memory optimization.

Understanding Data

• Everything in IT revolves around data.
• Programming languages, databases, AI are all about processing, storing, and understanding data.

Primitive Data Types

• Basic types to store numbers (integers), text (strings), characters.
• Different ways of storage depending on programming languages.

Introduction to Data Structures

• Purpose: Organize and store data efficiently.
• Efficiency: Related to performance and memory usage.
• Problem: Dumping data increases memory and search difficulty.

Algorithms

• Set of instructions to perform tasks (e.g., adding numbers, processing user actions).
• Algorithms can be language-agnostic (pseudo code) and need to be implemented in actual code for different languages.

Importance of Algorithms

• Make applications faster and cost-efficient.
• Enhance user experience.
• Part of a company's strategy to ensure efficient data handling.

Why Companies Focus on Data Structures and Algorithms (DSA)

• Helps in reducing computational costs.
• Provides better customer experience with fast applications.
• Filters candidates in hiring processes.

Summary on Data Structures

• Data structures organize and store data efficiently.
• Upcoming sessions will cover types, usage, and when to use specific data structures and algorithms.

Abstract Data Types (ADT)

• Conceptual models to handle data types.
• Examples include arrays, lists, sets, and queues.

Arrays

• Simple data structure to store data sequence.
• Operations include reading, searching, inserting, and deleting elements.
• Each operation affects time complexity differently.

Time Complexity

• Measure of algorithm efficiency based on input size.
• Types include O(1), O(n), O(log n), O(n^2), etc.
• Big O Notation is used to express time complexity.

Common Algorithms

• Linear Search: Check each element, time complexity O(n).
• Binary Search: Divide and conquer approach, time complexity O(log n).

Sorting Techniques

• Bubble Sort: Simple to implement, time complexity O(n^2).
• Selection Sort: Reduces swaps, time complexity O(n^2).
• Insertion Sort: Efficient for small data sets, involves shifting elements.
• Quick Sort: Divide and conquer, efficient, average time complexity O(n log n).
• Merge Sort: Stable sort using divide and conquer, time complexity O(n log n).

Linked Lists

• Dynamic data structure, nodes linked with pointers.
• Singly Linked Lists: One direction traversal.
• Doubly Linked Lists: Bi-directional traversal (forward and backward).

Stacks

• Last In First Out (LIFO) data structure.
• Operations: Push, Pop, Peek.
• Used in function call management and undo mechanisms.

Queues

• First In First Out (FIFO) data structure.
• Operations: Enqueue, Dequeue, Peek.
• Used in scheduling and resource sharing.

Trees

• Hierarchical data structure.
• Binary Trees: Each node has at most two children.
• Binary Search Trees (BST): Left child < parent, right child > parent.

This summary captures the essence of data structures and algorithms, their importance, various types, operations, and usage scenarios in software development.