Course Outline
Introduction
- High-level overview of AWS QuickSight capabilities.
- Clarifying the roles of AWS and QuickSight in the ecosystem.
Getting Started with AWS QuickSight
- Setting up your AWS and QuickSight accounts.
- Familiarizing yourself with the standard QuickSight workflow.
- Navigating the QuickSight user interface with confidence.
Preparing Data in QuickSight
- Exploring the nuances of data preparation within QuickSight.
- Comparing SPICE in-memory engine vs. direct query options.
- Processes for uploading and importing external data into QuickSight.
- Managing and manipulating columns and fields effectively.
- Mastering calculated fields, including relevant functions and operators.
- Integrating string-based calculated fields into your projects.
- Techniques for extracting specific information from string data.
- Applying conditional functions for dynamic data handling.
- Developing calculated fields that process numeric values.
- Implementing various filters to refine project datasets.
Analyzing and Visualizing Data
- Distinguishing between the data preparation and analysis phases.
- Constructing robust data analyses from prepared datasets.
- Designing effective visual elements to convey insights.
- Understanding the distinction between dimensions and measures.
- Incorporating additional datasets to enrich analytical models.
- Refining field formatting, aggregation methods, and granularity.
- Polishing visuals for clarity and professional presentation.
- Building interactive stories and treemap visualizations.
- Leveraging filters and tables to support detailed exploration.
- Integrating KPI visuals to highlight key performance metrics.
Exporting and Sharing Project Data
- Managing data refresh schedules and on-demand updates.
- Exporting project data into standard .csv file formats.
- Managing user access by adding members to your account.
- Sharing datasets and analytical work with colleagues.
- Creating and distributing comprehensive dashboards.
Using Databases as Data Sources
- Configuring database environments for integration.
- Generating and preparing dummy data for testing purposes.
- Establishing secure connections between QuickSight and your database.
- Loading data into the SPICE engine for high-performance querying.
- Importing data via direct queries for real-time access.
- Integrating calculated fields alongside complex queries.
- Utilizing NoSQL databases as alternative data sources.
Summary and Next Steps
Requirements
- A foundational understanding of data analysis principles and methodologies.
Target Audience
- Data analysts seeking to expand their technical toolkit.
- Professionals and enthusiasts interested in exploring data analysis and visualization techniques.
Testimonials (4)
Refactoring Exercise and Graph Visualization
Tiago Kocevar - Lang Energie AG / Osterwalder Zurich AG
Course - Business Intelligence and Data Analysis with Metabase
Machine Translated
Deepthi was super attuned to my needs, she could tell when to add layers of complexity and when to hold back and take a more structured approach. Deepthi truly worked at my pace and ensured I was able to use the new functions /tools myself by first showing then letting me recreate the items myself which really helped embed the training. I could not be happier with the results of this training and with the level of expertise of Deepthi!
Deepthi - Invest Northern Ireland
Course - IBM Cognos Analytics
he was well prepared - and he is very sympathetic
Oliver - Post CH AG
Course - Splunk Fundamentals
lots of pratical exercises
