Complete Guide to Python Data Structures & Algorithms Course

Refresh your memory of Python syntax and semantics with hello world, for loops over lists, and function definitions, and see code that prints to the console.

Explore fundamental data types in Python and core data structures, including lists manipulated with append and remove, with hands-on examples using variables and printing results.

Define and call Python functions, use the print function, and apply for loops and if-else logic to control flow and iterate over data.

Explore Python arrays and lists by creating and accessing lists, using zero-based indexing, and performing common operations like append, remove, and reverse on fruit and number lists.

Explore array operations in Python by accessing elements, inserting at specific indices, and deleting items with the pop method, including examples of first, last, and middle elements.

Learn to manipulate lists in Python by appending, removing, and reversing elements, and traverse lists with for loops, list comprehensions, and enumerate.

Analyze the time complexity of accessing and inserting elements in arrays and lists in Python, using start and end times to compare performance.

Learn how to implement a singly linked list in Python by creating a node class, linking nodes, and traversing the list to print each node's data.

Explore the structure and implementation of a doubly linked list in Python, including node design with data, previous and next references, head initialization, end insertion, and traversal display.

Explore insertion, deletion, and traversal operations in Python linked lists to understand nodes, pointers, and basic data-structure concepts.

Analyze the efficiency of linked list operations in Python by implementing insertion at the beginning and end, and by traversing the list to understand time complexity.

Examine Python implementations of stacks and queues, creating stack and queue classes with push, pop, peek, enqueue, and dequeue, and show outputs for top items and FIFO order.

Implement stacks and queues in Python using arrays and linked lists, demonstrating last-in, first-out and first-in, first-out behavior with push, pop, and peek operations.

Explore stack and queue concepts in Python, implementing push, pop, enqueue, and dequeue within a class, and learn how last-in, first-out and first-in, first-out behavior operate in code.

Explore the applications of stacks and queues by implementing a Python stack for expression evaluation with parentheses and operators, then simulate a print queue to manage and process jobs.

Explore binary trees and binary search trees in Python, learning how nodes, roots, left and right children form data structures and how insertion and search work.

Learn inorder, preorder, and postorder tree traversal algorithms in Python, including building a binary tree, implementing traversal functions, and printing traversal outputs.

Learn to implement a hash table in Python by defining a hash table class, a hash function, and insert and get methods to store and retrieve key-value pairs.

Master hashing in Python by implementing separate chaining and open addressing with linear probing, building hash tables, and performing insert and get operations.

Explore collision resolution in hash tables by implementing chaining, linear probing, and quadratic probing in Python, including insertion and search of key-value pairs.

Implement a hash table in Python, performing insertion, deletion, and searching with a hash function to compute indices and store key-value pairs.

Master comparison-based sorting with bubble sort, selection sort, insertion sort, merge sort, and quicksort in Python, exploring implementations and algorithm steps.

Explore time complexity analysis and stability of sorting algorithms in Python, comparing bubble sort and merge sort, showing how equal elements retain their order in ascending order for predictable outcomes.

Explore the linear search algorithm in Python, a sequential method that scans a list to find a target element by index, returning the position or indicating not found.

Learn how to implement binary search in Python, a fast algorithm that finds a target value within a sorted array by repeatedly dividing the search interval in half.

Implement interpolation search in Python for uniformly distributed sorted arrays, using an interpolation formula to estimate position and improve on binary search with practical code.

Compare linear, binary, and interpolation search with Python implementations, showing linear search checks elements sequentially, binary search halves the search interval, and interpolation search estimates position by value.

Explore the concept of recursion in Python, including base cases and recursive cases, with examples like factorial and Fibonacci to illustrate recursive algorithms.

Explore how dynamic programming uses overlapping subproblems and optimal substructure to solve complex problems by breaking them into subproblems, with memoization in Python to optimize time and space.

Explain the greedy algorithm in Python, highlighting locally optimal choices and the risk of missing global optima, illustrate with a coin change problem solved in descending coin order.