Aspect-Oriented Programming

Algorithms with Python, Finding Time-Space Complexity and Learning the basic algorithms with python

Created byAbhinav Raj

Last updated 9/2024

English

English [Auto],

What you'll learn

Python
Space Time Complexity
Big O Notation
Optimizations
Algorithms

Course content

1 section • 7 lectures • 1h 8m total length

Introduction4:09
Analyze space and time complexity across Python arrays, Rust vectors, and C++ structures, covering access, search, insertion, and deletion in lists, stacks, queues, hash tables, and binary search trees.
Sorting Algorithms7:04
Explore sorting algorithms, including merge sort, selection sort, and insertion sort, with array-based Python implementations and practice manipulating left and right halves and indices.
Sorting Algorithms19:33
Explore practical implementations of sorting and searching algorithms in Python, including merge sort and quicksort, and extend to binary search, breadth-first and depth-first search on graphs.
Algorithms10:37
Explore topological sorting on graphs and implement it with stacks, queues, arrays, and nodes, showcasing depth first search and visited checks.
Algorithms Implementation3:34
Explore and implement classic algorithms including bubblesort, insertion, selection, merge and quicksort, as well as graph traversals (bfs, dfs), shortest paths, and related data structures like trie.
Algorithms: Space Time Complexity6:06
Explore space and time complexity, including global time complexity, while using turtle graphics to draw nodes, lines, circles, and binary structures.
Algorithms: Space Time Complexity16:58
Explains time and space complexity while visualizing a binary tree with turtle graphics, showing how root, height, and branch length affect performance in a Python class.

Requirements

Prior programming experience is recommended.

Description

Welcome guys this is the second part of the Algorithmic Programming Series.

Dive into the world of algorithms with the powerful Python programming language in our "Algorithms 102 " course. This course is designed for those who wish to strengthen their understanding of algorithmic thinking while mastering Python's unique features. Through a blend of theoretical foundations and hands-on practice, students will learn how to solve complex problems efficiently and write high-performance code in a safe and concurrent environment.

What You Will Learn:

Foundational Concepts: Understand the core principles of algorithms, including complexity analysis (Big O notation), recursion, sorting, and searching.
Data Structures in Python: Explore how to implement essential data structures such as arrays, stacks, queues, linked lists, trees, and graphs in Rust, leveraging its ownership and type system for safety and efficiency.
Algorithmic Optimization: Gain proficiency in a variety of algorithmic techniques with a focus on learning.
Python-Specific Features: Learn to utilize Python high-level programming to implement algorithms that are not only correct but also fast and reliable.
Project-Based Learning: Apply what you've learned in a series of hands-on projects that challenge you to solve real-world problems using algorithms and Python, preparing you for software development in industries where performance and reliability are important.

Who this course is for:

Anyone who wants to implement algorithms in their day-to-day programming habits.