No-cost online AWS training pathway for researchers and research IT
To help researchers learn about cloud computing, Amazon Web Services (AWS) curated a list of no-cost, on-demand online courses tailored to researchers’ needs. AWS helps researchers process complex workloads by providing the cost-effective, scalable, and secure compute, storage, and database capabilities needed to accelerate time-to-science. Scientists can quickly analyze massive data pipelines, store petabytes of data, and share their results with collaborators around the world.
The AWS research team selected this list of courses from hundreds of available courses, specifically for researchers and research IT professionals who want to learn foundational cloud services. These online courses are available at any time to help users learn new cloud skills and services.
Research Learning Pathway: Foundational Services
The Research Learning Pathway: Foundational Services is designed for researchers and research IT professionals who want to become more proficient in optimizing research on AWS. Learn how to use the right storage medium, remove heavy lifting with managed services, and reproduce research with containers and software-defined infrastructure. This learning pathway can be completed in just over seven hours, and courses range in length from five minutes to three hours each. We recommend you complete the courses you need in the sequence outlined here.
- AWS Compute Services Overview (5 minutes): This course provides an overview of AWS compute services that empower you to build and run your business from the cloud in a way that suits your application needs.
- Introduction to AWS Batch (15 minutes): In this course, we discuss AWS Batch, which is a fully managed batching process service. We also cover the theory of operations behind AWS Batch, help you get familiar with its concepts, and walk through a demonstration of AWS Batch.
- Introduction to Containers (15 minutes): This is an introductory course designed for participants with little-to-no previous knowledge of containers. It will teach you the history and concepts behind containerization, provide an introduction to specific technologies used within the container ecosystem, and discuss the importance of containers in microservice architectures.
- Amazon Elastic Kubernetes Service (Amazon EKS) Primer (1 hour): This course teaches you the basics of the Amazon EKS. You will learn about the implementation of containers on AWS using Amazon EKS and complementary services, as well as communications and management in EKS.
- AWS Storage Offerings (1 hour 40 minutes): This course helps you distinguish the differences between the multiple AWS storage services and helps you consider the appropriate storage options for your applications that need access to data.
- Introduction to Amazon FSx for Lustre (10 minutes):This is an introductory course on Amazon FSx for Lustre, a fully managed service that makes it easy and cost-effective for AWS customers to launch and run a Lustre high-performance file system for their data-intensive applications. The course introduces you to the features and benefits of the service, such as its massively scalable performance, seamless integration with Amazon Simple Storage Service (Amazon S3), and compatibility with customer applications.
- Deep Dive into Amazon Elastic File System (Amazon EFS) (1 hour): In this course, you learn to use, manage, and secure Amazon EFS. You also learn about performance monitoring and optimization and pricing models, including Total Cost of Ownership (TCO). You also explore specialized AWS services, including AWS Direct Connect and Amazon CloudWatch.
- Deep Dive into Amazon Simple Storage Service (Amazon S3) (2 hours 30 minutes):This 300-level self-paced course provides you with the knowledge to use, manage, secure, monitor, and optimize Amazon S3 for your storage. You explore buckets, objects, security policies, and the storage management capabilities of Amazon S3. You discover other AWS security services and tools and how they can be used to help monitor and secure Amazon S3.
- Machine Learning in the Cloud with AWS Batch (30 minutes): This course describes how to run and accelerate your machine learning applications in the cloud using AWS Batch. This course provides an introduction to AWS Batch, explains the main components of the service, and reviews a few examples of batch processing architectures.
- AWS Hadoop Fundamentals (1 hour 30 minutes): AWS Hadoop Fundamentals introduces you to the basics of big data and how Hadoop as a framework handles it. This course discusses Hadoop architectures and how large sets of data are stored and processed. The course explains several tools used in the process: MapReduce, Hive, and Pig.
- Data Analytics Fundamentals (3 hours 30 minutes): In this course, you learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. This includes learning basic architectures, value propositions, and potential use cases.
- Deep Dive into Amazon Glacier (2 hours): This 300-level self-paced course introduces you to Amazon Glacier features, service options, and strategies. The course provides you with a functional understanding of the available long-term storage solutions and when to use them, and the knowledge to secure and migrate existing data from on-premises environments or within cloud platforms.
Additional learning pathways for researchers
AWS has additional learning pathways appropriate for researchers who want to dive deeper into AWS capabilities. These pathways include a blend of no-cost digital courses and synchronous training.
- Data Analytics: This learning path is designed for individuals who design and implement AWS services to derive value from data. Learn about collection, ingestion, storage, processing, and visualization. This pathway includes one classroom course: Big Data on AWS. Then get AWS Certified to validate the skills you’ve learned.
- Machine Learning Path/Data Scientist – Certification Available: This path is designed for learners skilled in math, statistics, and analysis who want to become machine learning subject matter experts within their university or research domain. This pathway includes two classroom courses – Practical Data Science with Amazon SageMaker and an Exam Readiness Course for Certified Machine Learning. Learn how machine learning frameworks and analysis tools can improve research collaboration.
- Machine Learning/Data Platform Engineer: This path is designed for data platform engineers. Learn machine learning to change data ingestion, system requirements and performance, and customer experiences.
- Machine Learning Exam Preparation: This path is designed specifically for individuals preparing to take the AWS Certified Machine Learning – Specialty exam. In addition to these self-paced digital training courses, we recommend one or more years of hands-on experience using machine learning (ML) services on AWS.
Applying the curriculum and accelerating research
AWS has been working closely with the National Institutes of Health (NIH) as part of their Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative. The STRIDES Initiative aims to modernize the biomedical research ecosystem by reducing economic and process issues with accessing commercial cloud services. NIH-funded biomedical researchers are eligible to leverage the STRIDES Initiative to access cutting-edge cloud services and tools, which includes access to favorable pricing on industry-leading commercial cloud environments from STRIDES Initiative partners, such as AWS. Training researchers on advanced computational infrastructure, tools, and services is an important part of the STRIDES Initiative.
“With the NIH STRIDES Initiative enabling the use of cloud computing resources and the Institutes and Centers actively developing cloud-based infrastructure tailored to their portfolios, investigators across the entire NIH biomedical spectrum are seeking to expand cloud computing expertise within their groups. A well-defined curriculum developed specifically for research scientists is a much-needed resource and will greatly accelerate the migration of our scientific workflows to the cloud,” said Janelle Cortner, Ph.D., Director, Data Management Program, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute (NCI).
For more information about joining the NIH STRIDES Initiative for AWS, visit the AWS partner Four Points Technology’s STRIDES Initiative website.
Review all no-cost digital training courses available from AWS. Learn more about AWS Training and Certification and research and technical computing on AWS.
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