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Course Outline

Introduction

  • Overview of AWS QuickSight.
  • Understanding AWS and QuickSight.

Getting Started with AWS QuickSight

  • Creating an AWS and QuickSight account.
  • Understanding the QuickSight workflow.
  • Navigating the QuickSight UI.

Preparing Data in QuickSight

  • Understanding data preparation in QuickSight.
  • SPICE vs. direct query.
  • Uploading and importing data to QuickSight.
  • Working with columns and fields.
  • Understanding calculated fields, functions, and operators.
  • Adding calculated fields using strings to our project.
  • Extracting information out of strings.
  • Using conditional functions.
  • Creating calculated fields with numeric values.
  • Adding different filters to a project.

Analyzing and Visualizing Data

  • Understanding the difference between preparing and analyzing data.
  • Creating the data analysis.
  • Creating visuals.
  • Understanding dimensions and measures.
  • Adding additional data sets.
  • Field formatting, aggregation, and granularity.
  • Formatting visuals.
  • Creating a story and treemap.
  • Using filters and tables.
  • Adding a KPI visual.

Exporting and Sharing Project Data

  • Understanding refresh and schedule refresh.
  • Exporting project data as .csv files.
  • Adding users to an account.
  • Sharing data set and analysis.
  • Creating and sharing dashboards.

Using Databases as Data Sources

  • Setting up a database.
  • Preparing dummy data.
  • Connecting QuickSight to a database.
  • Importing data into SPICE.
  • Importing data as a Query.
  • Importing calculated fields and query.
  • Using NoSQL databases.

Summary and Next Steps

Requirements

  • Basic knowledge and understanding of data analysis.

Audience

  • Data analysts.
  • Anyone interested in data analysis and visualization.
 14 Hours

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