Big-O Time Complexity in Python Code

4.7
estrellas
10 calificaciones
ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

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
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Data SciencepyplotPython ProgrammingBig-Oalgorithm analysis

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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.

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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