Enroll Course: https://www.coursera.org/learn/algorithms-part2
In the vast landscape of computer science, a solid understanding of algorithms and data structures is paramount for any aspiring programmer. Coursera’s ‘Algorithms, Part II’ course, a continuation of its foundational ‘Part I’, delivers precisely that with a rigorous yet accessible exploration of graph and string processing algorithms. This course, entirely free to audit, is a treasure trove of knowledge for anyone serious about optimizing their code and tackling complex computational problems.
The syllabus is comprehensive, diving deep into essential topics. It kicks off with an in-depth look at **Undirected Graphs**, covering representations like adjacency matrices and lists, and fundamental search algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS). The course then transitions to **Directed Graphs**, exploring their applications in areas like garbage collection and web crawling, and introducing algorithms for topological sorting and finding strongly connected components.
A significant portion is dedicated to optimization problems. The lectures on **Minimum Spanning Trees** introduce greedy algorithms and classic implementations like Kruskal’s and Prim’s algorithms. Following this, the **Shortest Paths** module provides a thorough analysis of Dijkstra’s algorithm for non-negative weights and the Bellman-Ford algorithm for graphs with negative weights, showcasing applications from content-aware fill to arbitrage.
Furthering its practical relevance, the course tackles **Maximum Flow and Minimum Cut** problems, detailing the Ford-Fulkerson algorithm and the max-flow min-cut theorem, with applications in bipartite matching and baseball elimination. The latter half of the course shines a light on string-specific algorithms. **Radix Sorts** are explored, including LSD, MSD, and the efficient 3-way radix quicksort, alongside suffix sorting. **Tries**, both multiway and ternary search tries, are presented as efficient symbol table structures for string keys, enabling operations like prefix matching.
The challenge of **Substring Search** is addressed with a progression from brute-force to the highly efficient Knuth-Morris-Pratt and Boyer-Moore algorithms, and the clever Rabin-Karp fingerprinting method. **Regular Expressions** are demystified through the lens of the grep algorithm, linking back to graph reachability concepts. The course also delves into **Data Compression** techniques like run-length coding, Huffman, and LZW, with practical Java implementations. Finally, the course touches upon theoretical underpinnings with **Reductions**, **Linear Programming (optional)**, and the crucial concepts of **Intractability**, including P, NP, and the famous P=NP question, providing context for algorithm efficiency and problem-solving boundaries.
While ‘Algorithms, Part II’ does not offer a certificate, its value lies in the depth of knowledge and practical skills it imparts. The free access makes it an exceptional resource for students, developers, and anyone looking to build a robust foundation in algorithmic thinking. The Java implementations provide hands-on experience, solidifying the theoretical concepts. If you’ve completed ‘Part I’ or have a foundational understanding of algorithms, this course is a highly recommended next step to truly master the art of efficient problem-solving.
Enroll Course: https://www.coursera.org/learn/algorithms-part2