Simulating Time Series Data by Parallel Computing in Python

ofrecido por
Coursera Project Network
En este Proyecto guiado, tú:

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

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 desarrollarás

Big DataPython ProgrammingSimulationParallel Computing

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. 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

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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