Simulating Time Series Data by Parallel Computing in Python

oferecido por
Coursera Project Network
Neste projeto guiado, você irá:

Learn how to find the rate of change of a time dependent parameter

Learn how to simulate large number of values using the starmap function

Learn how to simulate large datasets while maintaining the original correlation using a custom function passed to parallel processes

Clock1.75 hours
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

By the end of this project, you will learn how to simulate large datasets from a small original dataset using parallel computing in Python, a free, open-source program that you can download. Sometimes large datasets are not readily available when a project has just started or when a proof of concept prototype is required. In this project, you will learn how to find the rate of change of a time dependent parameter. Next, you will learn how to simulate large number of values using the starmap function. Lastly, you will learn how to simulate large datasets while maintaining the original correlation between columns using a custom function passed to parallel processes. In this project, you will generate 10000 time dependent samples from an initial dataset containing just 20 samples. In reality, you can use several parallel processes and can generate millions of new time dependent samples which can be used to experiment a new big data product or solution. Note: You will need a Gmail account which you will use to sign into Google Colab. 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 você desenvolverá

Big DataPython ProgrammingSimulationParallel Computing

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  1. Create a function to calculate the rate of change of a time series data

  2. Apply the above function on time series data files

  3. Simulate new values of rates using Pool's starmap function

  4. Define a function to simulate real world parameter values – part I

  5. Define a function to simulate real world parameter values – part II

  6. Initialize variables to start the parallel simulation

  7. Initiate and track the simulation using 2 parallel processes

  8. Create the final dataframe containing a time column

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Perguntas Frequentes – FAQ

Perguntas Frequentes – FAQ

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