Learning Amazon Web Services (AWS) QuickSight

Course details

Amazon Web Services (AWS) QuickSight is a powerful data analytics and visualization tool for monitoring data, analyzing trends, and making decisions. You can leverage ETL processes to get data, shape it into a viable form for calculations and analysis, then load the data into the visualization interface. From there, you can create visuals and charts to share the data trends and analysis to communicate it to a wider audience and key stakeholders. Join instructor Helen Wall in this course, as she shows how to use all the features of QuickSight. Learn how to connect to data sources, including Excel files, S3 buckets, and SQL Server; transform data and add calculations; load data into the QuickSight visualization interface; and create and format engaging visualizations and dashboards. Helen also explains how to share your work through dashboards that others can access on their computers or mobile devices, and share work outside the platform via email, exports, and embedded applications.

Topics include:
QuickSight overview
Connecting to AWS data sets and other data sources
Transforming data
Creating calculated and conditional fields
Loading data to dashboards
Creating and formatting visualizations
Configuring dashboards
Sharing your reports

Concepts

0. Introduction

01 - Understand your data with QuickSight
02 - What you should know

1. Getting Started with AWS QuickSight

03 - Introducing Amazon Web Services (AWS) and QuickSight
04 - Comparing cloud vs. desktop applications
05 - Introducing visual components

2. Extracting Data

06 - Overviewing supported data sources
07 - Leveraging super-fast, parallel, in-memory, calculation engine (SPICE)
08 - Connecting to files
09 - Connecting to AWS cloud services
10 - Connecting to corporate data sources
11 - Connecting to SaaS
12 - Understanding data source limitations and settings
13 - Challenge - Connecting to data
14 - Solution - Connecting to data

3. Transforming Data

15 - Renaming fields
16 - Removing fields
17 - Filtering rows
18 - Changing data types
19 - Creating calculated fields
20 - Adding conditional fields
21 - Setting up geospatial grouping
22 - Challenge - Transforming data
23 - Solution - Transforming data

4. Loading Data

24 - Creating data sets
25 - Sharing data sets
26 - Refreshing data
27 - Joining tables
28 - Deleting data sets

5. Creating Visualizations

29 - Creating visuals
30 - Exploring visualization options
31 - Aggregating measures
32 - Formatting visuals
33 - Sorting data logically
34 - Filtering visuals
35 - Adding color themes
36 - Leveraging conditional formatting
37 - Creating table calculations
38 - Challenge - Creating visualizations
39 - Solution - Creating visualizations

6. Configuring Dashboards

40 - Introducing visualization best practices
41 - Interacting between visualizations
42 - Drilling down into visuals
43 - Utilizing parameters
44 - Adding on-screen controls
45 - Creating stories
46 - Leveraging ML Insights
47 - Challenge - Configuring dashboards
48 - Solution - Configuring dashboards

7. Sharing Your Analysis

49 - Navigating dashboard of visualizations
50 - Emailing reports
51 - Viewing on a mobile device
52 - Exporting reports and data
53 - Setting up anomaly alerts
54 - Embedding dashboards

Conclusion

55 - Next steps for understanding your data