AWS Academy Data Analytics

Description:

AWS Academy Data Analytics comprise lab exercises to support existing big data lectures and courses that an institution is teaching or plans to teach. It is designed to help students prepare for entry-level roles in data analysis and visualization. The course provides institutions with lab exercises that will teach students how to analyze large data sets, create visual representations of that data, and publish those representations to dashboards. The course uses a case study approach to provide students with the opportunity to experience creating a real-world application of big data analysis.

Course Objectives
AWS Academy Data Analytics Labs teaches students how to:

Describe big data analytical concepts
Ingest, store, and secure data
Query a data store with manual schema specification
Query a data store with automated schema generation
Load and query data in a data warehouse
Visualize structured and unstructured data
Automate loading data into a data warehouse
Analyze unstructured data
Analyze IoT data

Duration:

Approximately 16 hours.
Intended Audience
Undergraduate, Graduate or Professional students studying Information Science, Computing, Business Analytics, or similar degree program

Admission Criteria:

Passing Leverify Data Analytics course and completing the capstone project will make you eligible for AWS Academy Data Analytics course. Top 50% students from Leverify Data Analytics will get this opportunity. It is free of cost.

Lab Exercises:

Lab 1	Ingesting Data Into Amazon S3
Lab 2	Querying Amazon S3 Data Using Amazon Athena
Lab 3	Transforming Data Using Amazon S3, AWS Glue, and Amazon Athena
Lab 4	Loading the Amazon Redshift Cluster With Data and Querying
Lab 5	Delivering Insights using Amazon QuickSight
Lab 6	Setting up and Executing a Data Pipeline Job to Load Data into Amazon S3
Lab 7	Using AWS IoT Analytics for Data Ingestion and Analysis
Lab 8	Streaming Data with AWS Kinesis Firehose, Amazon Elasticsearch Service, and Kibana