Simulating Time Series Data by Parallel Computing in Python

提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

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
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

Big DataPython ProgrammingSimulationParallel Computing

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

よくある質問

よくある質問

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。