Algorithms Specialization Course Syllabus

Overview: This Algorithms Specialization course from Stanford University, taught by Professor Tim Roughgarden, provides a rigorous foundation in algorithmic thinking and problem-solving techniques essential for technical interviews and real-world software challenges. The course spans approximately 4 weeks with a total commitment of 20-30 hours, combining theoretical concepts with practical coding assignments. Each module builds on core algorithmic paradigms, progressing from foundational strategies to advanced applications, and concludes with a comprehensive final project. Lifetime access ensures flexibility for in-depth learning.

Module 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms

Estimated time: 6 hours

• Asymptotic analysis and Big-O notation
• Divide-and-conquer algorithm design paradigm
• Mergesort, quicksort, and randomized selection
• Analysis of randomized algorithms for performance optimization

Module 2: Graph Search, Shortest Paths, and Data Structures

Estimated time: 6 hours

• Breadth-first search (BFS) and depth-first search (DFS)
• Dijkstra’s shortest path algorithm
• Bellman-Ford algorithm for negative edge weights
• Core data structures: heaps, stacks, queues, and balanced trees

Module 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

Estimated time: 6 hours

• Greedy algorithm principles and applications
• Kruskal’s and Prim’s algorithms for minimum spanning trees
• Dynamic programming for sequence alignment and optimization
• Problem decomposition and memoization techniques

Module 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

Estimated time: 6 hours

• Advanced shortest path algorithms including Johnson’s algorithm
• Introduction to NP-completeness and computational intractability
• Reductions and problem hardness classification
• Approximation algorithms for intractable problems

Module 5: Final Project

Estimated time: 8 hours

• Design and implement an algorithmic solution to a real-world problem
• Analyze time and space complexity using Big-O notation
• Compare multiple algorithmic approaches and justify design choices

Prerequisites

• Proficiency in at least one programming language (e.g., Python, Java, or C++)
• Basic understanding of discrete mathematics and proof techniques
• Familiarity with fundamental data structures like arrays, linked lists, and trees

What You'll Be Able to Do After

• Analyze algorithm efficiency using asymptotic notation
• Apply divide-and-conquer, greedy, and dynamic programming strategies to solve problems
• Implement and optimize graph algorithms for real-world applications
• Recognize NP-complete problems and apply practical workarounds
• Demonstrate strong algorithmic reasoning for technical interviews and system design