Data Structures and Algorithms from Zero to Hero

Introduction to algorithms defines an algorithm as a step-by-step solution to a problem and the backbone of programming, showing how it enables automated problem solving in daily tasks.

Explore popular algorithms for efficient coding, including bubble sort, merge sort, and quicksort, and compare sorting and searching techniques like linear and binary search, graph search algorithms, compression, and encryption.

Explore how big O notation measures how algorithm performance scales with input size, comparing constant time, logarithmic, linear, linearithmic, quadratic, and exponential growth. Identify how these types guide algorithm choice.

Explore common data structures like arrays, linked lists, stacks, queues, trees, and hash tables, and learn how their organization boosts memory, access, and algorithm performance.

Discover arrays, a fundamental data structure that stores elements in contiguous memory, supports index-based access and updates, with add, remove, iterate operations, and tradeoffs relative to other structures.

Master arrays as a fundamental data structure by declaring, accessing, and modifying elements in Python, using fixed-size, contiguous memory, zero-based indexing, and for loops to iterate, find the maximum value.

Explore linked lists, a node-based data structure connected by next pointers. Learn how to insert, traverse, and manage dynamic size, while comparing to arrays and noting advantages and limitations.

Explore how to build a basic singly linked list in Python, from node and linked list classes to append, display, and delete operations, with clear traversal and dynamic memory concepts.

Explore stacks, a LIFO data structure with push, pop, and peek, and see their role in recursion, backtracking, and expression parsing, plus a Python list implementation.

Master stacks with push, pop, and peek operations, and implement a Python stack using a list. See how pushes build the stack, and how pop removes the top item.

Explore the FIFO queue data structure, its front and rear, enqueue, dequeue, and peek, and its role in breadth first search, data streams, and operating systems scheduling.

Learn how queues use FIFO with Python's deque for efficient enqueue and dequeue. Compare stacks and queues and see uses in customer service, ticketing, and OS scheduling.

Explore how hash tables store and retrieve data in constant time using keys, values, and a hash function, and implement one in Python from scratch, handling collisions.

Demonstrates how hash tables, via Python dictionaries, store and retrieve key-value data with a hash function. Create, update, delete, and loop through entries like a phonebook.

Master recursion by learning how a function calls itself to solve smaller problems, enabling elegant solutions for factorial, fibonacci, and nested directory searches while noting memory and performance limits.

Learn recursion by building factorial and Fibonacci functions, define base cases, and verify results with tests that print the computed values.

Explore sorting algorithms and how they organize data to speed up searching and analysis. Compare bubble sort, insertion sort, selection sort, merge sort, and quicksort, and understand when sorting helps.

Explore how the bubble sort algorithm compares adjacent elements, swaps them, uses multiple passes to sort a list, with an optimized Python implementation.

Bubble sort sorts a list by comparing adjacent elements and swapping when the first is larger, moving the largest to the end, with a Python implementation.

Explore how merge sort uses divide and conquer to split, sort, and merge data, with a Python implementation and insight into its O(n log n) performance.

Master merge sort, a divide and conquer algorithm that splits a list into two halves, recursively sorts them, and merges into a single sorted list in Python.

Explore how the quicksort algorithm uses divide and conquer, partitioning with a pivot, and recursion to sort in place with o n log n time, including a Python implementation.

Explore quicksort, a divide and conquer algorithm that uses a pivot to partition data and recursively sort smaller and larger lists in Python for ascending order.

Explore the fundamentals of searching algorithms and how computers locate data. Compare linear search, binary search, and hashing, and note their use with sorted versus unsorted data and duplicates.

Explore how the unordered list search uses a simple linear scan to find an element, with a Python implementation and discussion of best, worst, and average case time complexity.

Explore linear search on unordered lists with a step-by-step Python implementation, including enumerate, returning the index or -1 when not found, and a preview toward more efficient searches.

Explore binary search as a divide-and-conquer algorithm for sorted lists, compare its o(log n) efficiency to linear search, and show a Python implementation with left, right, and mid pointers.

Master binary search on sorted data with a practical Python implementation that divides the search space in half to locate a target or indicate not found.

Master how to verify a list is sorted in ascending order by writing a Python function, testing cases, and using an all-based approach within data structures and algorithms.

Explore unique filtering with sets in Python, converting lists to sets to remove duplicates and convert back to lists, with examples and a one-liner solution.

Learn to count occurrences in a list using a hash table (dictionary) in Python, tallying each item and returning the counts.

Explore recursion to find the maximum value in a list, using a base case and a recursive step that compares the first element to the max of the rest.

learn to balance statements with stacks by implementing a Python function that uses a stack and a pairs dictionary to match opening and closing symbols, returning true or false.

Explore the final chapter that revisits algorithms and data structures, reviewing sorting (bubble, merge, quicksort), search (linear, binary), and structures (arrays, linked lists, stacks, queues, trees, hash tables), plus recursion.