Master Data Analytics with AWS: A Complete Learning Plan with Labs (Free Course)

 In today’s data-driven world, the ability to extract insights from raw data is a game-changer. Whether you’re a data enthusiast, analyst, or cloud developer, Amazon Web Services (AWS) offers a comprehensive learning path designed to equip you with the most in-demand data analytics skills.

Introducing the AWS Data Analytics Learning Plan (includes labs) — your roadmap to mastering modern data analytics using the AWS Cloud. This learning plan is free, hands-on, and perfect for learners at all levels.

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What’s Included in the Learning Plan?

The AWS Data Analytics Learning Plan is a curated series of courses and hands-on labs covering all major aspects of analytics on AWS. The content is designed and delivered by AWS experts.

Key Modules:

1. Introduction to Data Analytics on AWS
   Learn the basics of data analytics and the role AWS plays in modern data pipelines.

2. Data Collection and Ingestion
   Explore services like Amazon Kinesis, AWS Glue, and Amazon MSK to ingest and prepare data in real-time or batches.

3. Data Storage and Management
   Learn how to manage structured and unstructured data using Amazon S3, Amazon Redshift, and AWS Lake Formation.

4. Data Processing
   Gain practical knowledge of data processing with AWS Glue, Amazon EMR, and AWS Lambda.

5. Data Visualization
   Use Amazon QuickSight to create compelling dashboards and visual insights from your datasets.

6. Machine Learning Integration
   Understand how to integrate data analytics with Amazon SageMaker for predictive modeling.

7. Security and Governance
   Dive into the security and compliance best practices using AWS IAM, AWS KMS, and AWS Config.

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Why the Labs Are a Game-Changer

Theory is essential, but hands-on practice is what truly builds skills.

The labs included in this plan allow you to:

• Work in real AWS environments

• Build end-to-end pipelines

• Analyze large-scale datasets

• Apply machine learning models on real use cases

These labs simulate real-world tasks, making it ideal for beginners and professionals alike.

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Who Should Enroll?

This learning plan is perfect for:

• Aspiring data analysts and scientists

• Cloud engineers looking to specialize in analytics

• IT professionals upgrading their skills in cloud data platforms

• Students exploring career options in data and cloud computing

Outcomes You Can Expect

By completing the AWS Data Analytics Learning Plan, you’ll:

• Gain a solid foundation in data analytics

• Learn how to build scalable data pipelines on AWS

• Be prepared for AWS Data Analytics certification

• Stand out in the job market with cloud-native analytics skills

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Start Learning Now — It’s Free!

Don’t miss out on this opportunity to skill up in one of the fastest-growing fields. Whether you’re just getting started or sharpening existing skills, the AWS Data Analytics Learning Plan is your gateway to becoming a cloud analytics expert.

👉 Get started today for free:
🔗 Enroll now on AWS Skill Builder

For Certification: Getting Started with Data Analytics on AWS

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Introduction to Cloud Computing

 

Introduction to Cloud Computing by IBM – Your Gateway to the Cloud Era

Introduction to the Course

In today’s digital-first world, understanding cloud computing is no longer optional — it’s essential. IBM’s “Introduction to Cloud Computing” course, available on Coursera and other learning platforms, provides a beginner-friendly, industry-informed overview of how the cloud is transforming the way we store, access, and manage data and applications. Whether you’re a developer, IT professional, student, or curious learner, this course gives you a clear and structured path to understanding what the cloud is, how it works, and why it matters.

What Is Cloud Computing?

Cloud computing refers to the delivery of computing services over the internet. These services include servers, storage, databases, networking, software, analytics, and intelligence — all accessible on-demand and typically paid for as you go. This model removes the need for owning and maintaining physical hardware, enabling companies and individuals to scale quickly, reduce costs, and innovate faster.

In simple terms, it’s like renting computing power and storage the way you’d rent electricity or water — flexible, efficient, and scalable.

What You'll Learn

This course offers a solid foundation in cloud computing concepts, with the goal of making learners comfortable with the terminology, architecture, and service models used in cloud environments. By the end, you’ll understand:

The basic definition and characteristics of cloud computing

Service models: IaaS, PaaS, and SaaS

Deployment models: Public, Private, Hybrid, and Multicloud

Core cloud components like virtualization, containers, and microservices

Benefits and risks of using the cloud

Introduction to major cloud service providers (AWS, Azure, Google Cloud, IBM Cloud)

Use cases and industry applications

An overview of DevOps, serverless computing, and cloud-native development

These topics are presented in non-technical language, making it ideal for newcomers.

Cloud Service and Deployment Models

A key highlight of this course is the clear explanation of cloud service models:

Infrastructure as a Service (IaaS): Offers raw computing resources like servers and virtual machines. Example: AWS EC2.

Platform as a Service (PaaS): Provides platforms for developers to build and deploy applications without managing underlying infrastructure. Example: Google App Engine.

Software as a Service (SaaS): Delivers software applications over the internet. Example: Gmail, Dropbox.

You’ll also explore deployment models, including:

Public Cloud: Services offered over the public internet (e.g., AWS, Azure)

Private Cloud: Cloud services used exclusively by a single organization

Hybrid Cloud: A mix of public and private cloud environments

Multicloud: Using services from multiple cloud providers

These concepts are critical for making informed decisions about cloud strategy and architecture.

 Real-World Applications

The course does an excellent job of connecting theory to practice. You'll see how cloud computing powers:

Streaming platforms like Netflix and Spotify

E-commerce sites like Amazon and Shopify

Healthcare systems for storing patient data securely

Banking and finance for fraud detection and mobile apps

Startups and developers deploying scalable apps quickly

This context helps you understand the value of cloud computing across industries and job roles.

Key Technologies: Virtualization, Containers & Microservices

To deepen your understanding, the course introduces fundamental cloud-enabling technologies:

Virtualization: Creating virtual versions of hardware systems (e.g., Virtual Machines)

Containers: Lightweight, portable application environments (e.g., Docker)

Microservices: Architectural style that breaks apps into smaller, independent services

While not technical in-depth, this section helps you see how these tools work together in a cloud-native environment.

Security, Compliance, and Challenges

No conversation about the cloud is complete without addressing security and compliance. The course gives an overview of:

Common cloud security concerns (data breaches, misconfigurations)

Compliance standards (e.g., GDPR, HIPAA, ISO)

Identity and access management (IAM)

Shared responsibility model between the cloud provider and the customer

You’ll also learn about disaster recovery, data redundancy, and backups — all crucial aspects of reliable cloud solutions.

No-Code Hands-On Labs

Unlike more technical cloud courses, this introduction focuses more on concepts than coding. However, learners are given opportunities to:

Explore cloud platforms (like IBM Cloud) via simple user interfaces

Launch services and understand cloud console navigation

Work with simulated environments to reinforce learning

These hands-on elements give you a sense of how cloud platforms work, without overwhelming you with code.

Who Should Take This Course?

This course is ideal for:

Absolute beginners with no cloud or IT background

Business professionals seeking to understand cloud adoption

Students and career changers entering the tech field

Project managers, product owners, or sales professionals who work on cloud-based projects

Aspiring cloud engineers who want to build a foundation before jumping into certification tracks like AWS, Azure, or GCP

Certification and Career Benefits

Upon completion, you’ll receive a Certificate from IBM — a globally recognized tech leader. But more than the credential, you’ll walk away with practical knowledge that boosts your cloud literacy and helps you confidently participate in cloud-related discussions and decisions.

This is also a stepping stone to advanced certifications like:

IBM Cloud Essentials

AWS Cloud Practitioner

Microsoft Azure Fundamentals (AZ-900)

Google Cloud Digital Leader

What’s Next After This Course?

If this course sparks your interest in cloud computing, you can continue learning with:

Cloud Application Development with Python

DevOps and Cloud Native Development

Kubernetes Essentials

Cloud Security and Compliance

Cloud Architecture and Solutions Engineering

These advanced paths dive deeper into building, deploying, and securing cloud-native applications.

Join Now : Introduction to Cloud Computing

Final Thoughts

IBM’s "Introduction to Cloud Computing" is more than just a course — it’s an invitation to the future of technology. Whether you're aiming to grow your career, build your startup, or just stay current in the evolving tech world, cloud literacy is a must. This course gives you a clear, confident start with zero fluff and maximum clarity.

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Docker for Beginners with Hands-on labs

 

Docker for Beginners with Hands-On Labs – The Practical Guide to Containerization

Introduction to the Course

The course "Docker for Beginners with Hands-on Labs" is a practical, beginner-friendly introduction to containerization using Docker — one of the most essential tools in modern DevOps and software development. Whether you're a developer, sysadmin, cloud engineer, or simply someone curious about scalable deployment, this course helps you understand what Docker is, why it's revolutionizing software delivery, and how to use it effectively through hands-on practice. It’s a perfect launchpad for those new to containers and seeking to build a solid foundation with real-world applications.

Why Learn Docker?

Docker is a platform designed to simplify application development and deployment by allowing developers to package software into standardized units called containers. These containers include everything the application needs to run — code, libraries, dependencies — and can run anywhere, from a developer's laptop to a cloud server. Learning Docker equips you to build, ship, and run applications faster and more reliably, which is a huge advantage in today’s agile, cloud-native world. Companies like Netflix, PayPal, and Spotify use Docker extensively to scale their services efficiently.

Course Objectives

By the end of this course, learners will be able to:

Understand the core concepts behind containers and Docker

Install and configure Docker on different operating systems

Build, run, and manage Docker containers and images

Use Dockerfiles to automate image creation

Work with Docker volumes and networks

Understand the basics of Docker Compose for multi-container applications

Apply real-world use cases in hands-on labs

This isn’t just theory — each concept is paired with guided exercises to make sure you gain practical, job-ready experience.

Getting Started with Containers

The course starts with an intuitive explanation of what containers are, how they differ from virtual machines, and why they matter. You'll learn that containers are lightweight, fast, and portable, making them ideal for modern microservices architecture. Through analogies and visuals, the course breaks down complex infrastructure topics into easily digestible concepts, ensuring even complete beginners can follow along.

Docker Architecture and Components

Next, learners explore the Docker architecture, including the Docker Engine, Docker CLI, and Docker Hub. You’ll learn how the Docker client interacts with the daemon, how images are pulled from Docker Hub, and how containers are run from those images. The course walks you through commands to:

Pull official images from Docker Hub

Run containers in interactive or detached mode

Inspect, stop, and remove containers

This section lays the groundwork for more advanced operations later in the course.

Building Docker Images and Dockerfiles

One of Docker’s most powerful features is the ability to build custom images using a Dockerfile — a script that defines how your image is constructed. The course teaches how to:

Write simple and multi-stage Dockerfiles

Use base images effectively

Add environment variables and configuration

Optimize image size for production

You’ll build images for sample web apps, experiment with builds, and learn to troubleshoot when things go wrong. This is an essential step in making applications portable and reproducible.

Docker Volumes and Persistent Data

Containers are ephemeral by nature — meaning data is lost when the container stops — but that’s not ideal for most applications. This module introduces Docker volumes, which let containers persist and share data. You’ll learn how to:

Create and mount volumes

Use bind mounts for local development

Understand the differences between anonymous and named volumes

These concepts are particularly useful when running databases or any service that needs to retain state.

Docker Networks and Communication

For real applications, containers need to talk to each other. Docker provides built-in networking capabilities that let you isolate, link, or expose services as needed. You’ll explore:

Bridge, host, and overlay networks

Port mapping and linking containers

Container DNS and service discovery

Hands-on labs demonstrate how to connect a front-end container with a back-end API and a database, simulating real-world service orchestration.

Docker Compose: Multi-Container Applications

One of the highlights of the course is the introduction to Docker Compose, a tool that lets you define and run multi-container applications using a simple YAML file. You’ll learn to:

Create a docker-compose.yml file

Define services, networks, and volumes

Scale services using docker-compose up --scale

Bring the entire app up or down with one command

This module prepares you to build more complex, modular systems and is essential for modern DevOps workflows.

Hands-On Labs and Projects

Unlike many theory-heavy courses, this course emphasizes hands-on learning. Each concept is reinforced through interactive labs and practical assignments. For example:

Build and deploy a simple Python or Node.js app using Docker

Set up a multi-container stack with a web app and a database

Use logs and commands to troubleshoot running containers

These labs mimic real tasks you’d face in a development or DevOps role, helping you become job-ready.

Who Should Take This Course?

This course is perfect for:

Developers who want to simplify their dev environments

DevOps engineers and SREs getting started with containerization

System administrators looking to modernize infrastructure

Students and tech enthusiasts exploring cloud-native tools

No prior Docker experience is required, though basic knowledge of the Linux terminal and command-line operations is helpful.

Certification and Value

Upon completion, learners receive a certificate of completion that validates their ability to use Docker for containerizing applications and services. More importantly, you'll gain hands-on experience that is immediately applicable to real projects. Docker skills are increasingly requested in job listings across software engineering, DevOps, and IT operations — and this course provides a direct path to gaining them.

What Comes After This?

Once you’ve built a strong foundation in Docker, you can advance to:

Kubernetes for Orchestration

CI/CD pipelines using Jenkins and Docker

Docker Security and Image Scanning

Deploying containers on AWS, Azure, or GCP

Microservices architecture and container monitoring tools

The containerization journey doesn’t stop at Docker — it only starts there.

Join Now : Docker for Beginners with Hands-on labs

Final Thoughts

The "Docker for Beginners with Hands-on Labs" course is a well-structured, immersive way to get started with one of the most transformative technologies in modern software development. With its focus on practice over theory, it ensures you don’t just learn Docker — you use Docker. Whether you're trying to streamline your development process, deploy apps more reliably, or start a career in DevOps, this course offers the practical knowledge and confidence to move forward.

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Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition

 

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.

Key Features

Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines

Stay up to date with a comprehensive revised chapter on Data Governance

Build modern data platforms with a new section covering transactional data lakes and data mesh

Book Description

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.

You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.

By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!

What you will learn

Seamlessly ingest streaming data with Amazon Kinesis Data Firehose

Optimize, denormalize, and join datasets with AWS Glue Studio

Use Amazon S3 events to trigger a Lambda process to transform a file

Load data into a Redshift data warehouse and run queries with ease

Visualize and explore data using Amazon QuickSight

Extract sentiment data from a dataset using Amazon Comprehend

Build transactional data lakes using Apache Iceberg with Amazon Athena

Learn how a data mesh approach can be implemented on AWS

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Table of Contents

An Introduction to Data Engineering

Data Management Architectures for Analytics

The AWS Data Engineer’s Toolkit

Data Governance, Security, and Cataloging

Architecting Data Engineering Pipelines

Ingesting Batch and Streaming Data

Transforming Data to Optimize for Analytics

Identifying and Enabling Data Consumers

A Deeper Dive into Data Marts and Amazon Redshift

Orchestrating the Data Pipeline

Hard Copy: Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition

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DevOps on AWS: Release and Deploy

 

Build your subject-matter expertise

This course is part of the DevOps on AWS Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: DevOps on AWS: Release and Deploy

There are 2 modules in this course

AWS provides a set of flexible services designed to enable companies to more rapidly and reliably build and deliver products using AWS and DevOps practices. These services simplify provisioning and managing infrastructure, deploying application code, automating software release processes, and monitoring your application and infrastructure performance. 

The third course in the series explains how to improve the deployment process with DevOps methodology, and also some tools that might make deployments easier, such as Infrastructure as Code, or IaC, and AWS CodeDeploy.

The course begins with reviewing topics covered in the first course of the DevOps on AWS series. You will learn about the differences between continuous integration, continuous delivery, and continuous deployment. In Exercises 1 and 2, you will set up AWS CodeDeploy and make revisions that will then be deployed. If you use AWS Lambda, you will explore ways to address additional considerations when you deploy updates to your Lambda functions.

Next, you will explore how infrastructure as code (IaC) helps organizations achieve automation, and which AWS solutions provide a DevOps-focused way of creating and maintaining infrastructure. In Exercise 3, you will be provided with an AWS CloudFormation template that will set up backend services, such as AWS CodePipeline, AWS CodeCommit, AWS CodeDeploy, and AWS CodeBuild. You will then upload new revisions to the pipeline.

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DevOps on AWS: Operate and Monitor

 

Build your subject-matter expertise

This course is part of the DevOps on AWS Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: DevOps on AWS: Operate and Monitor

There are 2 modules in this course

The third and the final course in the DevOps series will teach how to use AWS Services to control the architecture in order to reach a better operational state. Monitoring and Operation are key aspects for both the release pipeline and production environments, because they provide instruments that help discover what's happening, as well as do modifications and enhancements on infrastructure that is currently running. 

This course teaches how to use Amazon CloudWatch for monitoring, as well as Amazon EventBridge and AWS Config for continuous compliance. It also covers Amazon CloudTrail and a little bit of Machine Learning for Monitoring operations!

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Exam Prep: AWS Certified Cloud Practitioner Foundations

 

What you'll learn

The four domains - Cloud Concepts, Security and Compliance, Technology and Billing and Pricing - for the AWS Certified Cloud Practitioner exam

Certification exam-level practice questions written by experts from AWS

Simulations designed to solidify understanding of cloud concepts you need to know for the exam

Join Free: Exam Prep: AWS Certified Cloud Practitioner Foundations

There are 4 modules in this course

This new foundational-level course from Amazon Web Services (AWS), is designed to help you to assess your preparedness for the AWS Certified Cloud Practitioner certification exam.  You will learn how to prepare for the exam by exploring the exam’s topic areas and how they map to both AWS Cloud practitioner roles and to specific areas of study. You will review sample certification questions in each domain, practice skills with hands-on exercises, test your knowledge with practice question sets, and learn strategies for identifying incorrect responses by interpreting the concepts that are being tested in the exam. At the end of this course you will have all the knowledge and tools to help you identity your strengths and weaknesses in each certification domain areas that are being tested on the certification exam. 

The AWS Certified Cloud Foundations Certification the AWS Certified Cloud Practitioner (CLF-C01) exam is intended for individuals who can effectively demonstrate an overall knowledge of the AWS Cloud independent of a specific job role. The exam validates a candidate’s ability to complete the following tasks: Explain the value of the AWS Cloud, Understand and explain the AWS shared responsibility model, understand security best practices, Understand AWS Cloud costs, economics, and billing practices, Describe and position the core AWS services, including compute, network, databases, and storage and identify AWS services for common use cases

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AWS Cloud Practitioner Essentials

 

What you'll learn

Understand the working definition of the AWS Cloud

Differentiate between on-premises, hybrid-cloud, and all-in cloud

Describe the basic global infrastructure of the AWS Cloud

Explain the benefits of the AWS Cloud

Join Free: AWS Cloud Practitioner Essentials

There are 7 modules in this course

Welcome to AWS Cloud Practitioner Essentials. If you’re new to the cloud, whether you’re in a technical or non-technical role such as finance, legal, sales, marketing, this course will provide you with an understanding of fundamental AWS Cloud concepts to help you gain confidence to contribute to your organization’s cloud initiatives. This course is also the starting point to prepare for your AWS Certified Cloud Practitioner certification whenever it’s convenient for you.

After you complete the course, you’ll understand the benefits of the AWS Cloud and the basics of its global infrastructure. You’ll be able to describe and provide an example of the core AWS services, including compute, network, databases, and storage. For the finance-minded, you’ll be able to articulate the financial benefits of the AWS Cloud, define core billing and pricing models, and learn how to use pricing tools to make cost-effective choices for AWS services.

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Migrating to the AWS Cloud

 

Build your subject-matter expertise

This course is part of the AWS Fundamentals Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Migrating to the AWS Cloud

There are 4 modules in this course

This introductory course is for anyone who wants a deeper dive into AWS migration. Whether you want to understand what services are helpful, need to plan a migration for your organization, or are helping other groups with their own migration, you will find valuable information throughout this course. The course sessions structure cloud migration through the three-phase migration process from AWS: assess, mobilize, and migrate and modernize. This process is designed to help your organization approach and implement a migration of tens, hundreds, or thousands of applications. By learning about this three-phase structure—and the various AWS tools, features, and services that can help you during each phase—you will complete this course with a better understanding of how to design and implement migrations to AWS.

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Architecting Solutions on AWS

 

Build your subject-matter expertise

This course is available as part of 

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Architecting Solutions on AWS

There are 4 modules in this course

Are you looking to get more technical? Are you looking to begin working in the cloud, but don’t know where to go next? Are you looking to up your game by prepping for the AWS Solutions Architect Associate Exam? Do you see yourself as a cloud consultant, but can’t quite envision how your days would be? Are you puzzled how to match a customer’s requirements with the right AWS services/solutions? If so, you are in the right place!! You’ll learn how to plan, think, and act like a Solution Architect in a real-life customer scenario.

In this course, you’ll get prepared to begin your career architecting solutions on AWS. Through a series of use case scenarios and practical learning, you’ll learn to identify services and features to build resilient, secure, and highly available IT solutions in the AWS Cloud. Each week, a fictional customer will present a different need. We will then review the options, choose the best one for the use case and walk you through the architecture design on a whiteboard. You’ll learn about event-driven architectures with a focus on performance efficiency and cost. You’ll then gain knowledge on how to architect a solution using many purpose-built AWS services. With this understanding, you’ll get a sense of hybrid architectures with a refined focus on reliability and operational efficiency. Finally, you’ll wrap up your learning by understanding a multi-account strategy centered on security and cost.

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AWS Cloud Solutions Architect Professional Certificate

 

What you'll learn

Make informed decisions about when and how to apply key AWS Services for compute, storage, database, networking, monitoring, and security.

Design architectural solutions, whether designing for cost, performance, and/or operational excellence, to address common business challenges.

Create and operate a data lake in a secure and scalable way, ingest and organize data into the data lake, and optimize performance and costs.

Prepare for the certification exam, identify your strengths and gaps for each domain area, and build strategies for identifying incorrect responses.

Join Free: AWS Cloud Solutions Architect Professional Certificate

Professional Certificate - 4 course series

This professional certificate provides the knowledge and skills you need to start building your career in cloud architecture and helps you prepare for the AWS Certified Solutions Architect - Associate exam. You will start by learning key AWS Services for compute, storage, database, networking, monitoring, and security, then dive into how to design architectural solutions, how to create and operate a data lake, and how to prepare for the certification exam.

The AWS Certified Solutions Architect – Associate certification showcases knowledge and skills in AWS technology across a wide range of AWS services. The certification focuses on the design of cost and performance optimized solutions and demonstrating a strong understanding of the AWS Well-Architected Framework. This AWS Certification is one of the top-paying IT certifications, per the 

 SkillSoft IT Skills and Salary report

. Per

 Enterprise Strategy Group

, surveyed AWS Certification holders credited their certification for their higher earnings (74%), increased confidence (87%), and increased influence among coworkers (79%).

To prepare for your AWS Certification exam, we recommend that — in addition to attaining this professional certificate — candidates review the free exam guide, sample questions, and AWS technical documentation (e.g. white papers and product FAQs) on the

 AWS Certified Solutions Architect - Associate exam page

 to understand what content and services are covered by the exam.

Applied Learning Project

Through 15 hands-on labs, you’ll use the AWS Management Console to apply skills learned in the videos. 

For example: 

In Architecting Solutions on AWS, you’ll use Amazon API Gateway, AWS Lambda, Amazon SQS, Amazon DynamoDB, and Amazon SNS to build a serverless web backend.

In Introduction to Designing Data Lakes, you’ll use Amazon S3, Amazon OpenSearch Service, AWS Lambda and Amazon API Gateway to create an Amazon OpenSearch Service Cluster. You’ll also use Amazon S3, Amazon EC2, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, Amazon Elasticsearch Service to create a data ingestion pipeline with the use of high-scale AWS Managed services. 

In Cloud Technical Essentials, you’ll design a 3-tier architecture using services like Amazon VPC, Amazon EC2, Amazon RDS with high availability and Elastic Load Balancing following AWS best practices. You’ll upload an architecture diagram laying out your design including the networking layer.

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AWS Cloud Technical Essentials

 

What you'll learn

Describe terminology and concepts related to AWS services     

Articulate key concepts of AWS security measures and AWS Identity and Access Management (IAM)    

You will learn to distinguish among several AWS compute services, including Amazon EC2, AWS Lambda, and Amazon ECS.  

Understand AWS database and storage offerings, including Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon S3.

Join Free: AWS Cloud Technical Essentials

There are 4 modules in this course

Are you in a technical role and want to learn the fundamentals of AWS? Do you aspire to have a job or career as a cloud developer, architect, or in an operations role? If so, AWS Cloud Technical Essentials is an ideal way to start. This course was designed for those at the beginning of their cloud-learning journey - no prior knowledge of cloud computing or AWS products and services required!

Throughout the course, students will build highly available, scalable, and cost effective application step-by-step. Upon course completion, you will be able to make an informed decision about when and how to apply core AWS services for compute, storage, and database to different use cases. You’ll also learn about cloud security with a review of AWS' shared responsibility model and an introduction to AWS Identity and Access Management (IAM). And, you’ll know how AWS services can be used to monitor and optimize infrastructure in the cloud.

AWS Cloud Technical Essentials is a fundamental-level course and will build your competence, confidence, and credibility with practical cloud skills that help you innovate and advance your professional future. Enroll in AWS Cloud Technical Essentials and start learning the technical fundamentals of AWS today!

Note: This course was designed for learners with a technical background. If you are new to the cloud or come from a business background, we recommend completing AWS Cloud Practitioner Essentials (https://www.coursera.org/learn/aws-cloud-practitioner-essentials) before enrolling in this course.

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Serverless Architectures on AWS

 

Build your subject-matter expertise

This course is part of the Developing Applications on AWS Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Serverless Architectures on AWS

There are 2 modules in this course

A modern software engineer knows how to use the benefits of managed services from Amazon Web Services to reduce the coding needed to get a project across the line. There’s a lot of code you really don’t need to write when you can use a managed service for your applications. Less code means less tests, less bugs, and quicker delivery. 

In this course, we get hands on with automation tools and serverless managed services. Get your projects completed faster without needing to maintain the underlying servers hosting the managed services. Treat your infrastructure as code using AWS CloudFormation and AWS Serverless Application Model as an automated way to build the resources hosting your applications. We use AWS Amplify to rapidly add front-end hosting and AWS Cognito to add authentication to our application. With Cognito in place, we upgrade the application API to require authentication. Next, we learn to use AWS Step Functions to move a lot of the workflow coordination code out of your applications. Using serverless services, we contrast some options for building event driven architectures with Amazon SNS, Amazon SQS and Amazon EventBridge. Join our expert instructors as we dive deep on real-world use cases for each of the featured services in the course. 

This course will provide a combination of video-based lectures, demonstrations and hands-on lab exercises that will get you working with automation tools, Cognito authentication, Step Function workflows and event-driven architectures.

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AWS Fundamentals Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from Amazon Web Services

Join Free: AWS Fundamentals Specialization

Specialization - 3 course series

This specialization gives current or aspiring IT professionals an overview of the features, benefits, and capabilities of Amazon Web Services (AWS). As you proceed through these four interconnected courses, you will gain a more vivid understanding of core AWS services, key AWS security concepts, strategies for migrating from on-premises to AWS, and basics of building serverless applications with AWS. Additionally, you will have opportunities to practice what you have learned by completing labs and exercises developed by AWS technical instructors.

Applied Learning Project

This specialization gives current or aspiring IT professionals an overview of the features, benefits, and capabilities of Amazon Web Services (AWS). As you proceed through these four interconnected courses, you will gain a more vivid understanding of core AWS services, key AWS security concepts, strategies for migrating from on-premises to AWS, and basics of building serverless applications with AWS. Additionally, you will have opportunities to practice what you have learned by completing labs and exercises developed by AWS technical instructors.

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Introduction to Machine Learning on AWS

 

What you'll learn

Differentiate between artificial intelligence (AI), machine learning, and deep learning. 

Select the appropriate AWS machine learning service for a given use case.

Discover how to build, train, and deploy machine learning models.

Join Free: Introduction to Machine Learning on AWS

There are 2 modules in this course

In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.

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DevOps on AWS: Code, Build, and Test

 

What you'll learn

Understand the DevOps philosophies and its lifecycle

Implement and manage continuous delivery systems and methodologies on AWS

How to use the right tools to measure code quality by identifying workflow steps

Join Free: DevOps on AWS: Code, Build, and Test

There are 2 modules in this course

DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes. This speed enables organizations to better serve their customers and compete more effectively in the market.

DevOps process can be visualized as an infinite loop, comprising these steps: plan, code, build, test, release, deploy, operate, monitor. Throughout each phase, teams collaborate and communicate to maintain alignment, velocity, and quality. This course in the DevOps on AWS specialization focuses on code, build and test parts of the workflow. We will discuss topics such as source control, best practices for Continuous Integration, and how to use the right tools to measure code quality, by identifying workflow steps that could be automated.

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Hands-on Machine Learning with AWS and NVIDIA

 

There are 4 modules in this course

Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.

Join Free: Hands-on Machine Learning with AWS and NVIDIA

This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting.

In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance. You will then learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model. You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases. After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks.

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Introduction to Designing Data Lakes on AWS

 

What you'll learn

Where to start with a Data Lake?

How to build a secure and scalable Data Lake?

What are the common components of a Data Lake?

Why do you need a Data Lake and what it's value?

Join Free: Introduction to Designing Data Lakes on AWS

There are 4 modules in this course

In this class, Introduction to Designing Data Lakes on AWS, we will help you understand how to create and operate a data lake in a secure and scalable way, without previous knowledge of data science! Starting with the "WHY" you may want a data lake, we will look at the Data-Lake value proposition, characteristics and components.

Designing a data lake is challenging because of the scale and growth of data. Developers need to understand best practices to avoid common mistakes that could be hard to rectify. In this course we will cover the foundations of what a Data Lake is, how to ingest and organize data into the Data Lake, and dive into the data processing that can be done to optimize performance and costs when consuming the data at scale. This course is for professionals (Architects, System Administrators and DevOps) who need to design and build an architecture for secure and scalable Data Lake components. Students will learn about the use cases for a Data Lake and, contrast that with a traditional infrastructure of servers and storage.

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Getting Started with Data Analytics on AWS

 

What you'll learn

Explain different types of data analyses – descriptive, diagnostic, predictive, prescriptive

Understand how to perform descriptive data analytics in the cloud with typical data sets

How to build simple visualizations in AWS QuickSight to do descriptive analytics (using S3, Cloudtrail, Athena)

Join Free: Getting Started with Data Analytics on AWS

There is 1 module in this course

Learn how to go from raw data to meaningful insights using AWS with this one-week course. Throughout the course, you’ll learn about the fundamentals of Data Analytics from AWS experts.

Start off with an overview of different types of data analytics techniques - descriptive, diagnostic, predictive, and prescriptive before diving deeper into the descriptive data analytics. Then, apply your knowledge with a guided project that makes use of a simple, but powerful dataset available by default in every AWS account: the logs from AWS CloudTrail. The CloudTrail service enables governance, compliance, operational auditing, and risk auditing of your AWS account. Through the project you’ll also get an introduction to Amazon Athena and Amazon QuickSight. And, you’ll learn how to build a basic security dashboard as a simple but practical method of applying your newfound data analytics knowledge.

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DevOps on AWS Specialization

 

What you'll learn

Implement DevOps culture and practices in the AWS Cloud

Adopt and enforce Continuous Integration and Continuous

Delivery best practices on AWS

Explore deployment strategies for serverless applications

Join Free: DevOps on AWS Specialization

Specialization - 4 course series

DevOps on AWS specialization teaches you how to use the combination of DevOps philosophies, practices and tools to develop, deploy, and maintain applications in the AWS Cloud. Benefits of adopting DevOps include: rapid delivery, reliability, scalability, security and improved collaboration.

The first course introduces you to essential AWS products, services, and common solutions. The course covers the fundamental concepts of compute, database, storage, networking, monitoring and security that learners and professionals will need to know when working with AWS.

The second course in the specialization discusses topics such as source control, best practices for Continuous Integration, and how to use the right tools to measure code quality, by identifying workflow steps that could be automated.

The third course explains how to improve the deployment process with DevOps methodology, and also some tools that might make deployments easier, such as Infrastructure as Code, or IaC, and AWS CodeDeploy.

Finally, the last course teaches how to use Amazon CloudWatch for monitoring, as well as Amazon EventBridge and AWS Config for continuous compliance. It also covers Amazon CloudTrail and a little bit of Machine Learning for Monitoring operations.

Applied Learning Project

AWS provides a set of flexible services designed to enable companies to more rapidly and reliably build and deliver products using AWS and DevOps practices. These services simplify provisioning and managing infrastructure, deploying application code, automating software release processes, and monitoring your application and infrastructure performance. This specialization has a significant hands-on component involving the AWS Free Tier in which you will explore AWS services and concepts using AWS SDKs, AWS APIs, and the AWS Console.

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