--- title: "Algorithms Boot Camp" description: "A structured learning path through data structures and algorithms fundamentals." url: "https://briansunter.com/algorithms-boot-camp" --- JUL 10, 2022 · 4 MIN READ # Algorithms Boot Camp A structured learning path through data structures and algorithms fundamentals.  Data structures and algorithms are among the most valuable concepts to learn in computer science. This boot camp organizes my notes into a structured learning path. ## About this guide I’m writing this from a student’s perspective, not an expert’s. I’ll be learning alongside you and solidifying my own knowledge as I go. Feel free to [correct me, make suggestions, and ask questions on Twitter](https://twitter.com/Bsunter). This guide is a living document. ## Why learn algorithms? Sometimes you’ll face complex problems at work that require deep knowledge of data structures and algorithms. Without understanding algorithms, you might: - Write code that runs extremely inefficiently - Build something that works for small inputs but breaks on large ones - Attempt to implement something that’s not theoretically possible to do efficiently Companies also ask algorithm questions in technical interviews. Even if you don’t use them daily, you should know them for interviewing. ## Who is this for? - Programmers who want to get better at algorithms - Experienced developers looking to solidify their knowledge - Anyone interested in learning how to take structured notes on code ## Roadmap ### Phase 1: Foundations #### Intro to Algorithms, Sorting, and Time Complexity [intro-to-algorithms](/intro-to-algorithms) - What is an algorithm? - Data structures 101 - Sorting and bubble sort [time-complexity](/time-complexity) - [A posteriori vs a priori analysis](/posteriori-vs-a-priori-analysis-of-algorithms) - Big O notation - Worst, average, and best case analysis - Recurrence relations - Arithmetic series - Bubble sort analysis **TypeScript Overview** - TypeScript intro - Intermediate TypeScript - Test-driven development - Design patterns #### Recursion **Fundamentals** - Recursion 101 - Base case - Inductive step - Recursive case **Examples** - Recursive exponent - Fibonacci numbers - Is palindrome - Pascal’s triangle **Backtracking** - Valid parenthesis - N-Queens problem **Limitations** - Call stack - Tail recursion #### Divide and Conquer - Merge sort #### Linked Lists **Linked List 101** - TypeScript implementation **Operations** - Insert - Delete - Find **Problems** - Reverse a linked list - Merge two sorted linked lists *** ## Complete topic reference This is a comprehensive list of all topics covered in the boot camp. ### Arrays **Concepts:** 2D arrays, n-dimensional arrays, jagged arrays **Problems:** Two sum, search index, kth largest element ### Linked lists **Operations:** Insert, delete, find, reverse, counting, finding middle, merging, kth to last, detecting cycles ### Stacks and queues **Stack:** Towers of Hanoi **Queue:** LIFO, FIFO ### Trees **Concepts:** Root, edge, leaf, depth **Binary trees:** Binary search trees, level order traversal, construct tree from traversal **Balanced trees:** AVL tree, B-tree **Operations:** Insertion, deletion, traversal (pre-order, in-order, post-order), search (BFS, DFS), find min/max, find successor/predecessor ### Hash tables **Implementations:** Array, linked list, binary tree **Operations:** Get, put, delete **Concepts:** Hash function, linear probing, quadratic probing, collision resolution **Problems:** LRU cache ### Heaps **Priority queue:** Compare function, enqueue, dequeue, peek **Concepts:** Complete binary tree, array representation, min heap, max heap **Operations:** Build, insert, get, delete, heapify, heap sort in place **Problems:** Merge k sorted lists, kth largest element, the skyline problem ### Tries Compressed trie, suffix trie ### Advanced data structures Skip list, suffix tree, suffix array, bloom filter ### Sorting Bubble sort, insertion sort, merge sort, quick sort, radix sort, counting sort, sort stability **Problems:** Dutch national flag, merge two sorted arrays ### Graphs **Concepts:** Graph theory, pathfinding, connectivity, shortest path, minimum spanning tree, cycles, disjoint graph **Traversal:** BFS, DFS, bipartite graph, topological sort **Algorithms:** Dijkstra’s, Bellman-Ford, Floyd-Warshall, Prim’s, Kruskal’s **Representations:** Adjacency list, adjacency matrix ### Recursion **Concepts:** Backtracking, base case, recursive case, inductive step, tail recursion, memoization **Problems:** Factorial, exponent, subsets, Fibonacci, Pascal’s triangle, Towers of Hanoi, binary strings, letter case permutations, permutations, subset sum, generate parenthesis, N-Queens, subarray sum equals k, valid parenthesis ### String algorithms **Concepts:** String matching, pattern matching, string search, data compression, edit distance, substring search, string similarity **Algorithms:** Rabin-Karp, KMP (Knuth-Morris-Pratt) **Problems:** Non-repeating characters, anagram, palindrome, frequency counting, reverse ### Dynamic programming **Problems:** 0/1 knapsack, longest common subsequence, subset sum, minimum edit distance, minimum coins, longest increasing subsequence, longest palindromic subsequence, text justification ### Network flow Maximum flow, minimum cut, Ford-Fulkerson, Edmonds-Karp, Dinic’s algorithm, König’s theorem ### NP-complete problems **Approximation methods:** Simulated annealing, tabu search, genetic algorithms, ant colony optimization, particle swarm optimization **Classic problems:** Set cover, knapsack, bin packing, vertex cover, Hamiltonian path, traveling salesman, satisfiability, partition, clique, chromatic number ### Search algorithms Linear search, binary search, DFS, BFS, best-first search, A\* search, Dijkstra’s, Bellman-Ford, Floyd-Warshall, Johnson’s algorithm ### Algorithm design Greedy algorithms, dynamic programming, divide and conquer, decrease and conquer, transform and conquer, randomized algorithms ### Algorithm analysis **Time complexity:** Big-O, Omega, Theta, little-o notation **Series:** Arithmetic, harmonic, arithmetic-geometric **Space complexity** ## Subscribe to newsletter I send occasional emails about new blog posts, side projects, and things I'm learning. By subscribing, you agree to our [Privacy Policy](/privacy). [Older Almanack of Naval Ravikant ](/almanack-of-naval-ravikant)[Newer Machine Learning Study Guide (GPT-3 Generated)](/machine-learning-study-guide-gpt) ## Related - [Binary Search Algorithm Jan 5, 2023](/binary-search) - [Heap, Heap Sort, Heapify, and Priority Queues Jan 5, 2023](/heap) - [Recurrence Relation and Master's Theorem for Dividing Functions Jan 5, 2023](/recurrence-relation-masters-theorem-dividing) ## Share this article