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via Udemy |
Go to Course: https://www.udemy.com/course/advanced-algorithms-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on algorithms and data structures in Python: --- **Course Review and Recommendation: Data Structures & Algorithms in Python on Coursera** If you're passionate about computer science and interested in mastering key algorithms and data structures in Python, this course offers an exceptional learning experience. Designed for both enthusiasts and aspiring professionals, the course systematically covers foundational concepts and guides you through implementing each structure from scratch. **Course Content Overview:** 1. **Binary Indexed Trees (Fenwick Trees):** Gain insights into this advanced data structure used for efficient frequency and prefix-sum computations, with practical applications in computer vision and AI. 2. **LRU Caches:** Understand the significance of caching, learn to implement Least Recently Used caches with doubly linked lists, and explore their real-world utility. 3. **Splay Trees:** Discover self-adjusting binary search trees and their role in optimizing repeated access patterns, with implementations tailored for cache management. 4. **B-Trees:** Dive into external memory data structures optimal for databases and file systems, understanding how to handle large datasets efficiently. 5. **Prefix Trees (Tries):** Explore string searching techniques, autocomplete algorithms, and IP routing with this versatile tree structure. 6. **Ternary Search Trees:** Learn about this variant of tries, useful in applications like spell checking and word games. 7. **Substring Search Algorithms:** Study essential algorithms like Rabin-Karp, Knuth-Morris-Pratt, and Z-algorithm for efficient string matching, crucial in text processing and search engines. 8. **Topological Ordering:** Understand how to order nodes in directed acyclic graphs (DAGs) — fundamental in scheduling and dependency resolution. 9. **Cycle Detection:** Learn methods to identify cycles in graphs, a key step in verifying graph properties. 10. **Strongly Connected Components & Tarjan’s Algorithm:** Delve into the identification of cyclic subgraphs, important in graph analysis. 11. **Hamiltonian Cycles & Traveling Salesman Problem:** Explore the classic NP-hard problem with backtracking and heuristic strategies. 12. **Eulerian Cycles & Chinese Postman Problem:** Understand cycle detection in graphs and optimize routes that traverse every edge. **Pros:** - Step-by-step implementation from scratch, deepening understanding. - Theoretical explanations combined with practical coding exercises. - Wide range of topics covering fundamental and advanced algorithms/data structures. - Clear links to real-world applications like AI, databases, networking, and scheduling. **Cons:** - The course is intensive; some may find the breadth overwhelming without prior background. - Advanced topics may require additional supplementary resources for full comprehension. **Who Should Enroll:** This course is perfect for students, developers, and researchers interested in algorithms, data structures, and Python programming. It’s especially recommended for those preparing for technical interviews, coding competitions, or working on software projects involving complex data management. **Final Verdict:** I highly recommend this course for anyone serious about understanding and implementing algorithms in Python. It offers a solid foundation, practical skills, and an engaging learning experience. Whether you're looking to deepen your knowledge or prepare for technical challenges, this course will equip you with valuable tools and insights. --- **Get started today and elevate your coding skills to the next level!**
This course is for those who are interested in computer science and want to implement the algorithms and given data structures in Python. In every chapter you will learn about the theory of a given data structure or algorithm and then you will implement them from scratch.Chapter 1: Binary Indexed Trees (Fenwick Trees)theory behind the binary indexed tree or Fenwick tree data structurehow to use this data structure in computer vision and artificial intelligenceimplementation in PythonChapter 2: LRU Caches what are caches and why are they so importanthow to use doubly linked lists to implement cachestheory behind LRU cachesimplementation in PythonChapter 3: Splay Treeswhat are splay treeshow to achieve caches with splay treesChapter 4: B-Treesexternal memory and internal memory (RAM)data structures for the external memorytrees with multiple children and multiple keyswhat are B-tree data structures?Chapter 5: Prefix Trees (Tries)what are tries or prefix treesreal world applications of triesautocomplete feature of triessorting with triesIP routingChapter 6: Ternary Search Treeswhat are ternary search treesboggle game with triesChapter 7: Substring Search Algorithmswhat are substring search algorithms and why are they important in real world softwaresbrute-force substring search algorithmhashing and Rabin-Karp methodKnuth-Morris-Pratt substring search algorithmZ substring search algorithm (Z algorithm)implementations in PythonChapter 8: Topological Orderingwhat is topological ordering (topological sort)?topological ordering implementation with depth-first searchChapter 9: Cycle Detectionhow to detect cycles in graphs?Chapter 10: Strongly Connected Components (Tarjan's Algorithm)what are strongly connected components?Tarjan's algorithm with depth-first searchChapter 11: Hamiltonian cycles (Travelling Salesman Problem)Hamiltonian cycles in graphswhat is the travelling salesman problem?how to use backtracking to solve the problemmeta-heuristic approaches to boost algorithmsChapter 12: Eulerian Cycles (Chinese Postman Problem)Eulerian cycles in graphswhat is the chinese postman problem?Thanks for joining my course, let's get started!