Simulating Time Series Data by Parallel Computing in Python

Offered By
Coursera Project Network
In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Big DataPython ProgrammingSimulationParallel Computing

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.