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学生对 Coursera Project Network 提供的 Big-O Time Complexity in Python Code 的评价和反馈

24 个评分
2 条评论


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....

1 - Big-O Time Complexity in Python Code 的 2 个评论(共 2 个)

创建者 Jorge G

Feb 26, 2021

I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material. Of course coursera gives me a small discount for having already paid it previously. It is very easy to download the videos and difficult to get hold of the material, but with ingenuity it is possible. Then I recommend uploading them to YouTube and keeping them private for when they want to consult (they avoid legal problems and can share with friends), then they can request a refund.

创建者 Kenn V G

Feb 7, 2022

This guided project has helped me understanding Big O notation in python, it's helpful to gain a fundamental understanding about this concept. I recommend whoever would like to have a basic understanding on Big O notation take part in this project.