Unit Outlines
Understand Algorithms And Data Structures
AI Generated
Intermediate
40 hours
10 topics
Learning Objectives
5 objectives- Understand fundamental concepts of algorithms and their significance in computer science.
- Analyze algorithm efficiency using time and space complexity metrics.
- Learn and implement key sorting and searching algorithms with an understanding of their performance.
- Explore and apply core data structures including arrays, linked lists, stacks, queues, trees, graphs, and hash tables.
- Develop problem-solving skills by selecting and using appropriate algorithms and data structures.
Content Outline
PreviewUnit 280: Algorithms and Data Structures
1. Introduction to Algorithms
- Definition and role of algorithms in computer science
- Importance of algorithms in problem-solving
- Characteristics of good algorithms
- Examples of simple algorithms
2. Complexity Analysis
- Concept of algorithmic complexity
- Time complexity: Big O notation
- Best, average, and worst-case scenarios
- Space complexity overview
- Analyzing and comparing efficiency of algorithms
3. Sorting Algorithms
- Overview and importance of sorting
- Bubble Sort
- Working principle
- Algorithm steps
- Complexity analysis
- Selection Sort
- Working principle
- Complexity analysis
- Insertion Sort
- Working principle
- Complexity analysis
- Merge Sort
- Divide and conquer strategy
- Algorithm steps
- Complexity analysis
- Quicksort
- Partitioning method
- Recursive approach
- Complexity analysis
4. Searching Algorithms
- Introduction to searching and its applications
- Linear Search
- Algorithm steps
- Complexity analysis
- Binary Search
- Preconditions: sorted data requirement
- Algorithm steps
- Complexity analysis
- Hash Tables
- Concept of hashing
- Handling collisions
- Use cases
5. Data Structures Overview
- Definition and importance of data structures
- Overview of fundamental data structures:
- Arrays
- Linked Lists
- Stacks
- Queues
- Trees
- Graphs
- Applications and performance considerations
6. Arrays and Linked Lists
- Arrays
- Structure and storage
- Access operations
- Advantages and limitations
- Linked Lists
- Types: singly, doubly, circular
- Node structure
- Insertion, deletion, traversal operations
- Comparison with arrays
7. Stacks and Queues
- Stacks
- LIFO principle
- Implementation using arrays and linked lists
- Applications (e.g., undo mechanisms, expression evaluation)
- Queues
- FIFO principle
- Implementation using arrays and linked lists
- Variants: circular queue, priority queue
- Applications (e.g., scheduling, buffering)
8. Trees and Binary Trees
- Tree data structure basics
- Binary Trees
- Properties and terminology
- Tree traversal methods (inorder, preorder, postorder)
- Binary Search Trees (BST)
- Structure and properties
- Insertion and deletion operations
- Searching in BST
- AVL Trees
- Self-balancing property
- Rotations to maintain balance
- B-Trees
- Multi-way search tree
- Applications in databases and filesystems
9. Graphs and Graph Algorithms
- Introduction to graphs
- Definitions: vertices, edges, directed/undirected
- Representations: adjacency matrix, adjacency list
- Graph traversal algorithms
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Shortest path algorithms
- Dijkstra's algorithm
- Minimum spanning tree algorithms
- Prim's algorithm
- Kruskal's algorithm
10. Hash Tables and Hashing
- Concept of hashing
- Hash functions and properties
- Collision resolution techniques
- Chaining
- Open addressing (linear probing, quadratic probing)
- Performance considerations
- Applications of hash tables
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