Big-O Time Complexity in Python Code
Use matplotlib Pyplot to produce a graph to visualize Big-O performance data.
Write and analyze the performance of a Bubble sort function.
Create a Binary Search function and perform Big-O analysis.
In the field of data science, the volumes of data can be enormous, hence the term Big Data. It is essential that algorithms operating on these data sets operate as efficiently as possible. One measure used is called Big-O time complexity. It is often expressed not in terms of clock time, but rather in terms of the size of the data it is operating on. For example, in terms of an array of size N, an algorithm may take N^2 operations to complete. Knowing how to calculate Big-O gives the developer another tool to make software as good as it can be and provides a means to communicate performance when reviewing code with others. In this course, you will analyze several algorithms to determine Big-O performance. You will learn how to visualize the performance using the graphing module pyplot. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Skills you will develop
- Data Science
- pyplot
- Python Programming
- Big-O
- algorithm analysis
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Use matplotlib’s Pyplot module to produce a graph to visualize Big-O performance data.
Write a function that returns one element and analyze the Big-O time complexity.
Write a Bubble sort function and analyze its performance.
Implement a Linear Search of an Array and determine its Big-O.
Create a Binary Search function and perform Big-O analysis.
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