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Course level: Fundamental
Duration: 2 hours Activities This course includes: lessons, videos, scenarios, and knowledge check questions.
Course objectives In this course, you will learn to: Explain data analytics, data analysis, analytics types, techniques, and analytics challenges. Define machine learning (ML), ML on AWS, and different levels of AWS for ML services. Define the 5 V’s of big data. Explain common ways to store data, challenges, characteristics of source data storage systems, and available AWS solutions. Explain data transportation, options for different environments, and available AWS solutions. Define data processing, options for each type of processing, and available AWS solutions. Identify different types of data structures, types of data storage, and available AWS solutions. Explain where ETL and ELT fits in multiple places of the analytics pipeline, the elements of an ETL and ELT process, and available AWS solutions. Explain the use of business intelligence tools to gain value from analytics, and available AWS solutions.
Intended audience This course is intended for: Cloud architects Data engineers Data analysts Data scientists Developers
Prerequisites We recommend that attendees of this course have: Reviewed AWS Cloud Practitioner Essentials or equivalent Course outline
Section 1: Introduction
Lesson 1: How to Use This Course
Lesson 2: Course Overview
Section 2: Analytics Concepts
Lesson 3: Analytics
Lesson 4: Machine Learning
Lesson 5: 5 Vs of Big Data
Lesson 6: Volume
Lesson 7: Variety
Lesson 8: Velocity
Lesson 9: Veracity
Lesson 10: Value
Section 3: AWS Services for Analytics
Lesson 11: AWS Services for Volume
Lesson 12: AWS Services for Variety
Lesson 13: AWS Services for Velocity
Lesson 14: AWS Services for Veracity
Lesson 15: AWS Services for Value
Section 4: Conclusion
Lesson 16: Quiz
Lesson 17: Course Summary
Lesson 18: Appendix of Resources
Lesson 19: Feedback
