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Course Outline

Introduction

  • AWS QuickSight overview
  • Definition of AWS and QuickSight

Initiating Use of AWS QuickSight

  • Establishing AWS and QuickSight accounts
  • Comprehending the QuickSight operational workflow
  • Navigating the QuickSight user interface

Data Preparation in QuickSight

  • Principles of data preparation within QuickSight
  • Comparison between SPICE and direct query methodologies
  • Methods for uploading and importing data to QuickSight
  • Management of columns and fields
  • Utilization of calculated fields, functions, and operators
  • Incorporation of string-based calculated fields into projects
  • Data extraction from string values
  • Application of conditional functions
  • Development of calculated fields utilizing numeric values
  • Implementation of various filters within a project

Data Analysis and Visualization

  • Distinguishing between data preparation and analysis processes
  • Execution of data analysis tasks
  • Generation of visual elements
  • Conceptualization of dimensions and measures
  • Inclusion of supplementary data sets
  • Configuration of field formatting, aggregation, and granularity
  • Styling of visual components
  • Development of stories and treemaps
  • Deployment of filters and tables
  • Integration of Key Performance Indicator (KPI) visuals

Data Export and Sharing Protocols

  • Mechanisms for refresh and scheduled updates
  • Exportation of project data as .csv files
  • Addition of users to an account for government access purposes
  • Distribution of data sets and analyses
  • Creation and dissemination of dashboards

Utilizing Databases as Data Sources

  • Configuration of database environments
  • Preparation of test data sets
  • Establishment of QuickSight connections to databases
  • Data importation into SPICE
  • Data importation via direct query methods
  • Incorporation of calculated fields and queries
  • Leverage of NoSQL databases for government data needs

Summary and Next Steps

Requirements

  • Foundational proficiency in analytical methodologies

Target Audience

  • Professional data analysts
  • Policymakers and stakeholders interested in data interpretation for government initiatives
 14 Hours

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