Data Structures and Algorithms Simplified Bootcamp

Master time complexity and Big O notation to compare algorithm efficiency as input size grows, covering constant, linear, quadratic, and logarithmic time with practical examples.

Explore space complexity and memory usage in algorithms, contrasting constant and linear space with arrays and insertion sort. See how memory scales with input size and relates to time complexity.

Explore arrays as a data structure of same-type elements in a contiguous memory block, learn zero-based indexing, constant time access, and basic operations such as insertion, deletion, and element access.

Explore how linked lists differ from arrays, with dynamic memory and non-contiguous storage. Learn singly, doubly, and circular lists, their traversal, insertion, deletion, and uses like round robin scheduling.

Explore linked list operations, including insertion, deletion, and searching, and learn to reverse a singly linked list. Visualize head and tail pointers, null terminators, and position-based insertions.

Learn to reverse a singly linked list by using three pointers—previous, current, and next—to flip each node's next reference, update head to the former tail, and traverse until end.

Explore stacks as a last-in, first-out data structure, learn push, pop, and peek operations, see a Python example with a list-based stack, and relate to backtracking, browser history, and DFS.

Learn to solve the balancing parentheses problem with stacks by traversing a string, pushing openings, and popping on matching closings; implement a Python solution and test edge cases.

Explore the queue data structure and the fifo principle, mastering enqueue and dequeue operations from the rear and front to ensure first in, first out processing.

Explore simple, circular, and priority queues and their fifo and priority-based behaviors. Learn how to enqueue and dequeue, and see Python examples with lists and the heapq library.

Generate binary representations from 1 to n using a queue in python, backed by a collections deque to maintain fifo order.

Explore binary trees and visit nodes once using preorder, in-order, and postorder traversals with root, left, and right children, and learn why each method supports copying, sorting, or deleting files.

Explore binary search trees, where left children are smaller and right children are greater or equal, enabling insertion and deletion across leaf, one child, and two children with in-order successors.

Validate a binary search tree by ensuring left subtrees are smaller and right subtrees are greater. Use range check or in-order traversal, and optimize with previous pointer to reduce space.

Understand heaps, a binary tree data structure used in priority queues and heapsort. Compare max heaps and min heaps, and learn insertion and deletion via heapify.

learn how heapsort sorts data by building a max heap from an array, a complete binary tree, repeatedly swapping the root with the last element and heapifying until sorted.

Learn to find the k largest elements using a min heap of size k. Compare this approach to brute force sorting and understand the time complexity O(n log k).

Hash tables store data as key-value pairs for fast retrieval by mapping keys to indices with a hash function, and resolve collisions via chaining or open addressing.

Use a hash table to find duplicates in an array by one-pass lookup. In Python, use a dictionary and duplicates list; it runs in O(n) time and O(n) space.

Explore the graph data structure with vertices and edges, learn adjacency matrix and adjacency list representations, and examine directed, undirected, weighted, and unweighted graphs used in social media and maps.

Explore graph traversal with breadth-first and depth-first search, using queues and stacks to visit nodes level by level or deepen paths, with applications like shortest paths and connected components.

Learn bubble sort, a simple algorithm that compares adjacent elements and swaps when out of order, sending the largest to the end.

Learn selection sort by selecting the minimum from the unsorted part and swapping it with the first unsorted element, expanding the sorted portion until the list is sorted.

Explore insertion sort, an efficient algorithm for small or nearly sorted data sets, building a sorted sublist by inserting each new element after shifting larger elements to the right.

Explore merge sort, a divide-and-conquer sorting algorithm that splits a list into halves, recursively sorts them, and merges them into a single sorted array with O(n log n) time.

Explore quicksort, a divide and conquer algorithm that uses a pivot to partition a list into elements smaller and greater than the pivot. Recursively sort sublists and combine with the pivot to form the final sorted list.

Explore how heapsort uses a binary heap to sort in place by building a max heap, swapping the root with the last element, and heapifying the remaining list until sorted.

Practice linear search, the simplest search algorithm that scans each element from the start until it finds the target or ends, usable on sorted or unsorted arrays, with O(n) time.

Binary search uses a divide and conquer approach on a sorted list, comparing the target to the middle element, halving the search space guided by left and right indices.

Explore greedy algorithms that pick locally optimal choices with no backtracking, and see when they guarantee global optimality via greedy properties, with examples like activity selection and Huffman coding.

Explore the activity selection problem in greedy algorithms, choosing talks that end earliest to maximize the number of non-overlapping talks you can attend.

Maximize value in the fractional knapsack using a greedy approach based on value-to-weight ratios. Take the highest ratio items first and fill the rest with fractions, totaling 240 dollars.

Explain Huffman coding as a greedy algorithm that combines the two least frequent characters to build a binary code tree, assigning shorter codes to frequent characters for text compression.

Explore the Huffman coding algorithm by building its pseudocode, constructing a frequency-based priority queue and Huffman tree, then encoding text and preparing a decoding map, with a Python implementation.

Implement the Huffman coding algorithm in Python by building the Huffman tree with a priority queue, generating binary codes, and encoding and decoding text for efficient compression, with complexity discussions.

Explore dynamic programming as an optimization technique for problems with overlapping subproblems, avoid recomputation, and compare memoization (top-down recursion) with tabulation (iterative) using Fibonacci, longest common subsequence, and knapsack examples.

Compare memoization and tabulation in dynamic programming, highlighting top-down recursion with a cache versus bottom-up table construction and their efficiency.