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學生對 Coursera Project Network 提供的 Big-O Time Complexity in Python Code 的評價和反饋

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課程概述

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....
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1 - Big-O Time Complexity in Python Code 的 3 個評論(共 3 個)

創建者 Jorge G

2021年2月26日

創建者 Kenn V G

2022年2月7日

創建者 Devan W

2022年9月15日