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This course in Data Structures & Algorithms was designed by Samson Tse, Varderes Barsegyan, Ryan Scopio, and Karan Bhamra.
Reviewed By: Stan Khaykin, Karan Bhamra and Thad House
Contributors: Jonathan Kennedy, Glen Husman, Jessica Plotkin, Jaren Mendelsohn, Robert Sandor, and Alexander Leontiev
At Great Minds Robotics there exists a subset of students and instructors that are ready to learn about data structures and algorithms. Many of the concepts in this curriculum are fundamentally important in computer science as a whole and are required to have a successful career in the field. After completing this course, students will learn how to think like a professional developer when it comes to data-related issues, as well as apply the proper techniques and concepts to real-world problems. Additionally, it will prepare them for coding competitions, intermediate college courses and even entry-level development jobs.
- An FAQ on why data structures, and algorithms are important.
- Concepts include: coding conventions and algorithm efficiency.
- A discussion of simple sorting techniques and their usage.
- Implementation assistance for bubble sort, selection sort, and insertion sort.
- A review of how arrays, array-backed lists and various linked lists are implemented in code and in memory.
- Lessons on singly, doubly, and circularly-doubly linked lists.
- Explanation and implementation of stack and queue data structures and their importance.
- Explanation of tree-based data structures.
- Lesson on binary search trees, and various tree traversals.
- An explanation of the usefulness of recursion and its place in sorting.
- Lessons on merge sort and quick sort.
- Explanation of data structures which self-organize in order to maintain efficiency.
- Lessons on heaps and heap sort.
- Lesson on self-balancing binary search tree known as avl tree.
- Implementation of recursive algorithms for tree operations and tree traversals.
- Lesson on skip list, a probabilistic data structure that is built upon the idea of a linked list which allows for fast searches.
- Introduction to graph theory and the types of problems that graphs solve.
- Lessons on building undirected/unweighted and directed/weighted graphs.
- Lessons on pathfinding/search algorithms such as Dijkstra's, Bellman-Ford and a-star (A*).
- Explanation of hashing algorithms and how to apply them to the hashmap and hashset data structures.
- Lesson on the union-find (disjoint set) data structure and how to perform set operations such as find and union.
- Lesson on trie (prefix tree) and their usefulness in storing characters in strings as keys.
- Introduction to the probabilistic data structure called bloom filter and how it is utilized in modern systems.
- Lesson on caches, cache policies, and implementation of the least recently used (LRU) cache data structure.
- Lesson on huffman coding, which is used for lossless data compression.
- Lesson on the sorted set data structure which provides a total ordering on its elements, including set operations.
- Lesson on a generalized form of the binary search tree known as b-tree.
- Introduction to a left-leaning red black tree, which is a binary search tree that utilizes an extra bit to keep the tree balanced.