Data Structures and Performance course Syllabus

This course explores how data structures influence program performance and teaches techniques for writing efficient, high-performance code. Through a blend of theory and hands-on implementation, you'll analyze algorithmic complexity, compare data structure trade-offs, and apply optimization strategies. The course spans approximately 12–14 weeks with a weekly commitment of 6–8 hours, combining video lectures, coding exercises, and quizzes. By the end, you’ll complete a final project that demonstrates your ability to optimize real-world software problems.

Module 1: Algorithmic Complexity and Performance

Estimated time: 14 hours

• Introduction to Big-O, Big-Theta, and Big-Omega notations
• Analyzing time and space complexity of algorithms
• Identifying performance bottlenecks in code
• Comparing growth rates of functions

Module 2: Linear Data Structures

Estimated time: 18 hours

• Implementation of arrays and dynamic arrays
• Design and use of linked lists (singly and doubly)
• Stacks and queues: array-based vs linked implementations
• Performance comparison: dynamic vs static memory allocation

Module 3: Trees and Tree Traversal

Estimated time: 16 hours

• Binary trees and binary search trees
• Tree traversal techniques (in-order, pre-order, post-order)
• Performance analysis of tree operations
• Balanced trees: concepts and importance

Module 4: Hashing and Hash Tables

Estimated time: 16 hours

• Hashing fundamentals and hash functions
• Collision resolution: chaining and open addressing
• Load factor and rehashing strategies
• Optimizing data retrieval with hash tables

Module 5: Performance Optimization Techniques

Estimated time: 14 hours

• Selecting optimal data structures for specific problems
• Analyzing memory usage patterns and cache efficiency
• Applying algorithmic improvements in practical scenarios
• Comparing trade-offs between time and space efficiency

Module 6: Final Project

Estimated time: 20 hours

• Design and implement a performance-critical application
• Analyze and justify data structure choices
• Optimize for speed and memory efficiency

Prerequisites

• Basic knowledge of programming in Java or C++
• Familiarity with fundamental programming constructs (loops, conditionals, functions)
• Previous exposure to object-oriented programming

What You'll Be Able to Do After

• Understand how data structures impact program performance
• Analyze time and space complexity using Big-O notation
• Implement and optimize core data structures efficiently
• Compare trade-offs between different data storage approaches
• Strengthen problem-solving skills for technical interviews and software engineering roles