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via Udemy |
Go to Course: https://www.udemy.com/course/algorithms-and-data-structures-in-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on Data Structures, Algorithms, and Graphs: --- **Course Title: Data Structures, Algorithms, and Graphs in Python (Coursera)** **Course Overview:** This comprehensive course offers a deep dive into essential data structures, algorithms, and graph-related problems using Python. Designed for learners who want to build a strong foundation in computer science, the course meticulously covers both theoretical concepts and practical implementations, making it highly suitable for aspiring programmers, software engineers, and data enthusiasts. **What You Will Learn:** - **Fundamentals and Environment Setup:** Understanding the differences between data structures and abstract data types, and setting up a proper development environment. - **Core Data Structures:** Arrays, linked lists (singly and doubly), stacks, queues, binary search trees (BSTs), balanced BSTs like AVL and Red-Black trees, heaps, hash tables, and dictionaries. - **Graph Algorithms:** Traversing graphs with BFS and DFS, shortest path algorithms like Dijkstra’s and Bellman-Ford, spanning trees with Kruskal’s and Prim’s algorithms, and solving complex problems like Hamiltonian cycles and the Traveling Salesman Problem. - **String Search Algorithms:** Various string matching techniques including brute-force, Rabin-Karp, KMP, and Z algorithms. - **Sorting Algorithms:** From simple bubble sort and insertion sort to advanced quicksort, merge sort, counting sort, and radix sort. - **Algorithm Analysis:** Understanding how to evaluate algorithm efficiency using big O, big Ω, and big θ notations, and classifying algorithms into polynomial and NP categories. **Course Structure and Teaching Style:** The course is well-structured, starting with foundational topics and gradually advancing to complex algorithms. Each section combines theoretical explanations with step-by-step Python coding exercises. The instructor emphasizes the importance of practicing by re-implementing algorithms multiple times to solidify understanding—a practice highly recommended for mastering these concepts. **Why I Recommend This Course:** - **Thorough Coverage:** It covers a broad spectrum of fundamental algorithms and data structures crucial for technical interviews, software development, and research. - **Hands-On Approach:** Coding along in Python helps reinforce concepts and prepares students for real-world application. - **Versatility and Relevance:** Data structures and algorithms are foundational across many fields, including AI, finance, electronic trading, and more. Python’s extensive libraries complement the skills gained. - **Preparedness for Technical Interviews:** Many of the interview questions are addressed directly, giving you practical tools for your career. **Final Thoughts:** This course is an excellent investment for anyone looking to strengthen their understanding of core computer science concepts. Whether you are a beginner or someone looking to brush up on advanced topics, the blend of theory and practice ensures thorough learning. Remember, mastering data structures and algorithms takes practice, so perform multiple coding exercises to deepen your grasp. **Recommendation:** Enroll in this course if you are ambitious about building a solid foundation in algorithms and data structures or preparing for technical interviews. The skills learned here are not only academically enriching but highly applicable across various technological fields. --- Feel free to ask if you need assistance with specific topics or further guidance!
This course is about data structures, algorithms and graphs. We are going to implement the problems in Python programming language. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.So what are you going to learn in this course?Section 1:setting up the environmentdifferences between data structures and abstract data typesSection 2 - Arrays:what is an array data structurearrays related interview questionsSection 3 - Linked Lists:linked list data structure and its implementationdoubly linked listslinked lists related interview questionsSection 4 - Stacks and Queues:stacks and queuesstack memory and heap memoryhow the stack memory works exactly?stacks and queues related interview questionsSection 5 - Binary Search Trees:what are binary search treespractical applications of binary search treesproblems with binary treesSection 6 - Balanced Binary Trees (AVL Trees and Red-Black Trees):why to use balanced binary search treesAVL treesred-black treesSection 7 - Priority Queues and Heaps:what are priority queueswhat are heapsheapsort algorithm overviewSection 8 - Hashing and Dictionaries:associative arrays and dictionarieshow to achieve O(1) constant running time with hashingSection 9 - Graph Traversal:basic graph algorithmsbreadth-firstdepth-first searchstack memory visualization for DFSSection 10 - Shortest Path problems (Dijkstra's and Bellman-Ford Algorithms):shortest path algorithmsDijkstra's algorithmBellman-Ford algorithmhow to detect arbitrage opportunities on the FOREX?Section 11 - Spanning Trees (Kruskal's and Prim's Approaches):what are spanning treeswhat is the union-find data structure and how to use itKruskal's algorithm theory and implementation as wellPrim's algorithmSection 12 - 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 PythonSection 13 - 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 algorithmsSection 14 - Sorting Algorithmssorting algorithmsbubble sort, selection sort and insertion sortquicksort and merge sortnon-comparison based sorting algorithmscounting sort and radix sortSection 15 - Algorithms Analysishow to measure the running time of algorithmsrunning time analysis with big O (ordo), big Ω (omega) and big θ (theta) notationscomplexity classespolynomial (P) and non-deterministic polynomial (NP) algorithmsO(1), O(logN), O(N) and several other running time complexitiesIn the first part of the course we are going to learn about basic data structures such as linked lists, stacks, queues, binary search trees, heaps and some advanced ones such as AVL trees and red-black trees.. The second part will be about graph algorithms such as spanning trees, shortest path algorithms and graph traversing. We will try to optimize each data structure as much as possible.In each chapter I am going to talk about the theoretical background of each algorithm or data structure, then we are going to write the code step by step in Python.Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market. Research institutes use Python as a programming language in the main: there are a lot of library available for the public from machine learning to complex networks.Thanks for joining the course, let's get started!