Untitled Quiz

Data Structure and Algorithm (BCSE202L)

• Course offered by Dr. Durgesh Kumar at SCOPE, VIT Vellore
• Course code: BCSE202L
• Week 1: Lecture 01

Introduction to Data Structures

• A data structure is a way to store and organize data in a computer so it can be accessed and updated efficiently.
• Data structures are categorized as linear and non-linear.
• Examples include arrays, linked lists, stacks, queues, trees, and graphs.

Data Structure Examples

• Array: Data stored at contiguous memory locations
• Linked List: Data stored in nodes, each with data and a pointer to the next node.
• Stack: LIFO (Last In, First Out) data structure
• Queue: FIFO (First In, First Out) data structure
• Tree: Hierarchical structure with a root node, parent/child nodes, and leaf nodes; often used for searching and sorting
• Graph: A collection of nodes (vertices) and edges.

Data Structure Classification

• Linear: sequential arrangement
  • Static: Array
  • Dynamic: Stack, Queue, Linked List
• Non-Linear: hierarchical arrangement
  • Tree
  • Graph

Goals

• Categorize and differentiate different data structures as linear and non-linear.

Introduction to Algorithms

• Algorithm: A set of steps to accomplish a particular task.
• Examples: Finding a book in a library, getting a match on Tinder, getting maximum likes/followers on TikTok, choosing a gadget.
• Algorithm analysis: Evaluating time and space complexity, different cases (best, worst, average), and asymptotic notations (Big-O, Theta, Omega).

Next Class

• Algorithm analysis: Time and space complexity
• Best case, Worst case, Average case
• Asymptotic analysis: Big O, Theta, Omega
• Linear vs Non-linear data structure

Examples of Interesting Algorithms

• Live video transmission (Hangouts, Zoom): Audio and video compression algorithms.
• Route finding (Google Maps): Dijkstra's algorithm.
• 3D character coloring (Pixar): Rendering algorithms (Flat Shading, Phong Shading).
• Solar panel arrangement (NASA): Optimization and scheduling algorithms.
• Autocomplete/spell checking: Sophisticated algorithms.

Algorithm Design

• Designing computer games (e.g., checkers): Understanding algorithms to efficiently make game moves.
• Biological science applications: Drug candidate discovery.
• Geotech/civil applications: Climate and weather prediction.
• Astronomy applications: Finding new stars, analyzing huge data.

What makes a good algorithm?

• Correctness: Providing the correct solution(s).
• Efficiency: Finding the best solution, quickest execution time.
• Data Analysis applications: Analyzing massive data sets (e.g., Hubble telescope data).

Asymptotic Analysis (Big-O, Omega, Theta )

• Big-O (Upper Bound)
• Omega (Lower Bound )
• Theta (Tight Bound )
• Common used notations.

Recursion

• Explanation and examples.

Recurrence Relations

• Definition and examples.

Important Data Types

• Primitive: int, float, char, boolean
• User-Defined: struct, union, enum, class
• Derived: functions, arrays, pointers, references

Data Types in C/C++

• Classifications
  • Primitive
  • Derived
  • User-defined

Abstract Data Types (ADTs)

• Simplifying the problem-solving process.