Big-O Time Complexity in Python Code

Coursera Project Network

In the field of data science, mastering the Big-O time complexity is crucial for optimizing algorithms operating on large datasets. In this course, you will delve into the principles of Big-O performance and gain hands-on experience in analyzing algorithms.

  • Utilize matplotlib Pyplot to produce visual representations of Big-O performance data, enhancing your ability to communicate and interpret algorithm efficiency.
  • Explore the performance of a Bubble sort function, creating and analyzing its efficiency using Big-O notation.
  • Implement a Binary Search function and conduct a comprehensive Big-O analysis to gauge its performance on large datasets.

By the end of this course, you will have a solid understanding of Big-O time complexity, enabling you to optimize algorithms and effectively communicate performance metrics to peers and stakeholders.

Certificate Available ✔

Get Started / More Info
Big-O Time Complexity in Python Code
More Algorithms Courses

Accelerated Computer Science Fundamentals

University of Illinois at Urbana-Champaign

Accelerated Computer Science Fundamentals is designed to help students prepare for an Online Master of Computer Science entrance exam. Topics include object-oriented...

Machine Learning: Algorithms in the Real World

Alberta Machine Intelligence Institute

Machine Learning: Algorithms in the Real World is a comprehensive specialization that equips professionals with the skills to define, train, and maintain successful...

Features and Boundaries

Columbia University

This course delves into feature and boundary detection in images, vital for various vision tasks. It covers methods for detecting edges, corners, interest points,...

Tracking Objects in Video with Particle Filters

Coursera Project Network

Track and locate objects in real-world video using Python and numpy to code a particle filter from scratch.