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Transform and Clean your Data with Dataprep by Trifacta on Google Cloud

Offered By: Google via Qwiklabs

Tags

Google Cloud Platform (GCP) Courses Data Analysis Courses Data Visualization Courses BigQuery Courses Data Cleaning Courses Data Transformation Courses Data Preparation Courses Cloud Storage Courses Machine Learning Pipelines Courses

Course Description

Overview

Dataprep is Google's self-service data preparation tool built in collaboration with Trifacta. Learn the basics of cleaning and preparing data for analysis and visualization, all in the Google ecosystem. In this quest, you will learn how to connect Dataprep to your data in Cloud Storage and BigQuery, clean data using the interactive UI, profile the data, and publish your results back into the Google ecosystem. You will learn the basics of data transformation, including filtering values, reshaping the data, combining multiple datasets, deriving new values, and aggregating your dataset.

Syllabus

  • Working with Cloud Dataprep on Google Cloud
    • Cloud Dataprep is Google's self-service data preparation tool. In this lab, you will learn how to use Cloud Dataprep to clean and enrich multiple datasets using a mock use case scenario of customer info and purchase history.
  • Preparing and Aggregating Data for Visualizations using Cloud Dataprep
    • Dataprep by Trifacta is Google's self-service data preparation tool built in collaboration with Trifacta. In this lab you will learn some more advanced techniques with Dataprep.
  • Creating Advanced Data Transformations using Cloud Dataprep
    • In this lab, you will build upon a previous flow and learn some advanced tactics for preparing data.
  • Automating your BigQuery Data Pipeline with Cloud Dataprep
    • In this lab, you will examine how Dataprep can be used on complicated data structures in BigQuery.
  • Self Service ML Pipelines Using Dataprep and AutoML Tables
    • In this lab you will learn how to use Dataprep in conjunction with AutoML Tables to build and operate your machine learning pipelines.

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