Big-O Time Complexity in Python Code

4.6
별점
11개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Data SciencepyplotPython ProgrammingBig-Oalgorithm analysis

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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