Data Structures and Algorithms: Complete Developer’s Guide

Explore core array operations in python, including creating, accessing, updating, appending, inserting, removing, searching, traversing, reversing, sorting, and copying, while understanding time costs and O(1) vs O(n) performance.

Explore singly and doubly linked lists, learn how nodes connect via next and previous pointers, and implement insertion, traversal, and bidirectional navigation in Python.

Explore insertion, deletion, and traversal in arrays and linked lists using Python, including dynamic arrays, index-based inserts, pops, and node-pointer updates.

Detect cycles in linked lists and graphs using Floyd’s tortoise and hare, with slow and fast pointers meeting and the cycle start found in Python with no extra memory.

Explore the stack data structure with a last-in, first-out approach, implement push and pop in Python, and apply stacks to reverse strings, validate balanced parentheses, support undo, and backtracking.

Master first‑in, first‑out queues and circular queues, illustrating Python implementation tips and the efficiency gains from deque over lists in real‑world task and buffering scenarios.

Explore deques and priority queues in Python with practical code, including double-ended insertion, palindrome checks, and a simple task manager that demonstrates min-heap behavior and negative value tricks.

Learn recursion by building functions that solve smaller problems with a base case, visualize the call stack, and compare it to loops using factorial, sum to n, reverse, and fib.

Master the backtracking template to explore options, backtrack, and build solutions step by step. Apply it to subsets, permutations, and combination sums in Python while understanding constraints and path management.

Explore backtracking as a practical Python approach through permutations, subsets, and the N-Queens puzzle, using a clear template to make choices, dive deeper, and backtrack.

Explore binary trees and binary search trees, building a node-based structure, inserting values to maintain BST order, and using in-order traversal to obtain a sorted output, plus search and deletion.

Learn binary tree traversals in Python with inorder, preorder, and postorder patterns, and see code you can type out and test.

Learn how AVL trees stay balanced with rotations to enable fast binary search, and how tries support prefix-based word searches and autocompletion with simple insert and search demos.

Explore min and max heaps and how heapq enables a min heap while simulating a max heap. Learn push and pop operations for quick access to smallest or largest elements.

Explore how priority queues use heaps to pop highest-priority items first, with examples in scheduling and file searching; learn min-heap and max-heap tricks using Python heapq and negative priorities.

Explore hash tables and hash maps in Python, showing how a hash function maps keys to indices to store and retrieve key-value data efficiently in dictionaries.

Explore collision resolution in hash tables by implementing chaining and open addressing, including linear probing, to handle collisions with multiple items.

Explore frequency counting with a hash map to count word occurrences in text, and implement a basic lru cache for fast lookup and managing recently used items.

Explore three classic sorting algorithms: bubble sort, selection sort, and insertion sort, through hands-on demonstrations of swapping, selecting the smallest element, and inserting values.

Explore merge sort and quick sort through divide and conquer, learning how merge sort splits into single elements and merges sorted lists, while quick sort partitions around a pivot.

Explore counting sort, which counts non-negative integers within a range to build a sorted list without comparisons, then radix sort, which sorts digits using a stable method.

Explore linear search, a fundamental algorithm that scans a list or array to locate a target, returning its index or -1 if not found, and its applicability to unsorted lists.

Explore binary search on sorted data, halving the search space with low, high, and mid pointers. Learn variants for first/last occurrence and lower/upper bounds for insertion points and ranges.