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Big-O Time Complexity in Python Code(으)로 돌아가기

Coursera Project Network의 Big-O Time Complexity in Python Code 학습자 리뷰 및 피드백

4.6
별점
26개의 평가

강좌 소개

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....
필터링 기준:

Big-O Time Complexity in Python Code의 3개 리뷰 중 1~3

교육 기관: Jorge G

2021년 2월 26일

교육 기관: Kenn V G

2022년 2월 7일

교육 기관: Devan W

2022년 9월 15일