Big-O Time Complexity in Python Code

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在此指導項目中,您將:

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.

Clock1 hour
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Data SciencepyplotPython ProgrammingBig-Oalgorithm analysis

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Use matplotlib’s Pyplot module to produce a graph to visualize Big-O performance data.

  2. Write a function that returns one element and analyze the Big-O time complexity.

  3. Write a Bubble sort function and analyze its performance.

  4. Implement a Linear Search of an Array and determine its Big-O.

  5. Create a Binary Search function and perform Big-O analysis.

指導項目工作原理

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