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Course Outline

Introduction

  • Overview of AWS QuickSight
  • What is AWS and QuickSight?

Getting Started with AWS QuickSight

  • Creating an AWS and QuickSight account
  • Understanding the QuickSight workflow
  • Navigating the QuickSight user interface

Preparing Data in QuickSight

  • Understanding data preparation in QuickSight
  • SPICE versus 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 from strings
  • Using conditional functions
  • Creating calculated fields with numeric values
  • Applying different filters to a project

Analysing and Visualising Data

  • Understanding the difference between preparing and analysing data
  • Creating 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 scheduled refresh
  • Exporting project data as .csv files
  • Adding users to an account
  • Sharing data sets 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 queries
  • Using NoSQL databases

Summary and Next Steps

Requirements

  • Basic knowledge and understanding of data analysis

Audience

  • Data analysts
  • Anyone interested in data analysis and visualisation
 14 Hours

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