Data Structures Course Syllabus

Overview: This course provides a comprehensive introduction to essential data structures used in software development and algorithmic problem-solving. Over approximately 10 hours of content, learners will progress through foundational and advanced topics, combining theoretical understanding with hands-on programming assignments. The course is designed for intermediate learners and emphasizes practical implementation across multiple programming languages, including C++, Java, and Python.

Module 1: Basic Data Structures

Estimated time: 4 hours

• Arrays and their memory representation
• Singly and doubly linked lists
• Stacks and queues: operations and use cases
• Trees and tree traversals (preorder, inorder, postorder)

Module 2: Dynamic Arrays and Amortized Analysis

Estimated time: 1 hour

• Dynamic array implementation and resizing strategies
• Amortized analysis: aggregate method
• Banker’s method for amortized cost analysis
• Physicist’s method for amortized cost analysis

Module 3: Priority Queues and Disjoint Sets

Estimated time: 5 hours

• Priority queues and their applications
• Binary heaps and heap operations
• Disjoint set data structures: union and find operations
• Applications in scheduling and shortest-path algorithms

Module 4: Hash Tables

Estimated time: 3 hours

• Hash table design and collision handling
• Chaining and open addressing techniques
• Average-case O(1) performance analysis

Module 5: Graphs and Advanced Representations

Estimated time: 4 hours

• Graph representations: adjacency list and matrix
• Applications in file storage optimization and connectivity problems
• Trade-offs between different graph storage methods

Module 6: Final Project

Estimated time: 5 hours

• Implement a real-world problem solver using multiple data structures
• Optimize performance using appropriate data structure choices
• Submit and review code with automated feedback

Prerequisites

• Familiarity with basic programming concepts in C++, Java, or Python
• Understanding of basic algorithmic complexity (Big O notation)
• Experience with writing and debugging code

What You'll Be Able to Do After

• Implement core data structures such as arrays, linked lists, stacks, queues, trees, and graphs
• Analyze performance trade-offs between different data structures
• Apply dynamic arrays and amortized analysis to manage memory efficiently
• Use priority queues and disjoint sets to solve algorithmic problems
• Solve real-world challenges like scheduling and data retrieval using appropriate data structures