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AWS Data Analytics Fundamentals Course Overview

AWS Data Analytics Fundamentals Course Overview

The AWS Data Analytics Fundamentals course is designed to introduce learners to the core concepts and solutions of data analytics within the AWS ecosystem. It covers the essentials of data analysis, addressing the common challenges that professionals face in the field. The course is structured into modules covering the key dimensions of data analytics: Volume, Velocity, Variety, Veracity, and Value.

Participants will explore various AWS services, such as Amazon S3 for data storage, learn about Data Lakes, and understand different Data Storage Methods. They will delve into data processing techniques, including batch and Stream Processing, and examine different Data Structures, from structured to semi-structured and unStructured Data stores. The course also details Data Cleansing, ETL Processes, and the fundamentals of Reporting and Business Intelligence to ensure data integrity and extract meaningful insights. Lastly, the course wraps up with key takeaways and guidance on the next steps in the data analytics journey. Engaging with the AWS Data Analytics Fundamentals course can empower learners with the knowledge to harness AWS tools for effective data analysis.

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Course Prerequisites

Certainly! Below are the minimum required prerequisites for successfully undertaking the AWS Data Analytics Fundamentals course:


  • Basic understanding of IT services and their applications in an enterprise environment.
  • Familiarity with fundamental concepts of data processing and storage.
  • Awareness of basic data analytics concepts, including the difference between data analytics and data analysis.
  • Some experience with or exposure to databases and data storage methods is helpful but not mandatory.
  • Knowledge of core AWS services, especially Amazon S3, is beneficial but not required to begin the training.
  • An interest in learning how to process and analyze large sets of data using AWS services.

Remember, these prerequisites are designed to ensure that you have a foundational level of knowledge that will allow you to grasp the course material effectively. They are not meant to be barriers but rather guidelines to help you maximize your learning experience. If you find any of the concepts unfamiliar, we encourage you to engage in self-study or seek introductory resources before starting the AWS Data Analytics Fundamentals course.


Target Audience for AWS Data Analytics Fundamentals

The AWS Data Analytics Fundamentals course is designed to equip learners with the basics of AWS data analytics and storage solutions.


  • Data Analysts
  • Business Intelligence Professionals
  • Data Engineers
  • IT Professionals looking to specialize in data analytics
  • Data Science Enthusiasts
  • AWS Cloud Practitioners
  • Database Administrators
  • Solution Architects
  • Graduates aiming for a career in data analytics
  • Software Developers interested in data processing methods
  • System Administrators wanting to understand data storage
  • Product Managers looking to leverage data insights
  • Marketing Analysts interested in data-driven decision-making
  • Data Consultants seeking knowledge in AWS data services
  • Academic Researchers using data analytics for their work
  • Data Management Professionals
  • Enterprise Architects
  • Professionals transitioning to roles involving data analysis


Learning Objectives - What you will Learn in this AWS Data Analytics Fundamentals?

Introduction to the AWS Data Analytics Fundamentals Course Learning Outcomes

This course provides foundational knowledge in AWS data analytics, focusing on concepts, storage, processing, and visualization to drive business insights.

Learning Objectives and Outcomes

  • Understand the core principles of data analytics and the challenges involved in data analytic practices.
  • Gain knowledge of Amazon S3 and its role in data storage within the AWS ecosystem.
  • Learn the concepts of data lakes and their importance in managing large volumes of data.
  • Explore various data storage methods and their application in handling the 'Volume' aspect of big data.
  • Comprehend the different data processing methods, including batch and stream processing, addressing the 'Velocity' of data.
  • Understand the differences between structured, semi-structured, and unstructured data stores and their use in handling 'Variety' in data.
  • Grasp the importance of data integrity and database consistency for ensuring 'Veracity' in data analytics.
  • Get introduced to the Extract, Transform, Load (ETL) process for data cleansing and transformation.
  • Learn about the tools and techniques for analyzing and visualizing data to derive 'Value' and actionable business intelligence.
  • Synthesize the course content to understand how to apply AWS data analytics solutions in real-world scenarios and prepare for further advanced learning in data analytics.

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