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Questions and Answers
What is a data structure?
What is a data structure?
A data structure is a storage that is used to store and organize data, allowing it to be accessed and updated efficiently.
Give two examples of data structures.
Give two examples of data structures.
Array and Linked List
Which of the following is an example of a non-linear data structure?
Which of the following is an example of a non-linear data structure?
What is the characteristic of a stack data structure?
What is the characteristic of a stack data structure?
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A queue is a type of linear data structure.
A queue is a type of linear data structure.
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What does BFS stand for in data structures?
What does BFS stand for in data structures?
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Which of the following is used for shortest path algorithms?
Which of the following is used for shortest path algorithms?
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What is the primary use of a binary search tree?
What is the primary use of a binary search tree?
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What is the key operation of hashing?
What is the key operation of hashing?
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In a tree, the node with no children is called a __________.
In a tree, the node with no children is called a __________.
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Study Notes
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.
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