Master in Cloud Computing Online

The aim of this module is to give students the basic knowledge they need regarding cloud computing and learn how to express their value proposition.

• Innovation Culture
• The Art of the Possible
• Cloud Fundamentals
• Cloud Security
• Economies of Scale
• Cloud Economics and Law

10h.

In this module, students explore the services, applications and use cases of cloud computing. Students delve deeper into cloud computing and discover the role cloud computing can play in the development of global infrastructures to support large scale use cases, while developing and inventing innovative technologies.

• Describing a cloud service provider (CSP) and the value they bring to IT.
• Description of the basic security and compliance aspects of the AWS platform and the shared security model.
• Definition of billing, account management and pricing models.
• Identification of sources of technical documentation or support, for example, white papers or support tickets.
• Description of the fundamental features of deployment and operation in the AWS cloud.
• Identifying situations where a company should opt for the cloud, and why.
• Differentiation between on-premise and cloud infrastructure.
• How to migrate resources from on-premise infrastructure to cloud infrastructure.

42h.

In this module, students explore the services, applications and use cases of cloud computing. Students dive deep into cloud computing and discover the role cloud computing can play in the development of global infrastructures to support large scale use cases, while developing and inventing innovative technologies.

• Description of the AWS cloud and basic global infrastructure.
• Description of the basic architectural principles of the AWS cloud
• Description of the value proposition of the AWS cloud.
• Description of the key services of the AWS platform along with the common use cases (e.g. IT and analysis).
• Put key services into use in practical laboratory activities, including:
  • Amazon Simple Storage Service (Amazon S3)
  • Amazon CloudFront
  • AWS Lambda
  • Amazon Elastic Compute Cloud (Amazon EC2)
  • Amazon Virtual Private Cloud (Amazon VPC)
  • Amazon Comprehend
  • AWS DeepRacer
  • AWS CloudFormation

42h.

The course is aimed at students seeking a comprehensive understanding of cloud computing concepts, regardless of specific technical functions. It offers a detailed overview of core AWS services, security, architecture, pricing, and support.

• Defining the AWS cloud.
• Explanation of the AWS pricing philosophy
• Description of the components of the global AWS infrastructure.
• Description of security and compliance measures in the AWS cloud, including AWS Identity and Access Management (IAM).
• Creating a virtual private cloud (VPC) using Amazon Virtual Private Cloud (Amazon VPC).
• When to use Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and AWS Elastic Beanstalk.
• Differentiating between Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), Amazon Elastic File System (Amazon EFS) and Amazon Simple Storage.
• Service Glacier (Amazon S3 Glacier)
• When to use AWS database services, including Amazon Relational Database.
• Service (Amazon RDS), Amazon DynamoDB, Amazon Redshift y Amazon Aurora
• Explanation of the architectural principles of the AWS cloud.
• Explore key concepts related to Elastic Load Balancing, Amazon CloudWatch, and Amazon EC2.
• Auto Scaling

35h.

Cloud Architecting deals with the fundamental concepts of building IT infrastructure on AWS. This module enables students to optimize the use of the AWS cloud by understanding AWS services and how they fit into cloud-based solutions.

• Students learn to make architectural decisions based on AWS architectural principles and best practices.
• Using AWS services to make the infrastructure scalable, reliable, and highly available.
• Using AWS managed services to provide greater flexibility and resilience in an infrastructure.
• Increasing the performance and reducing the cost of a cloud infrastructure built on AWS.
• Using the AWS Well-Architected Framework to improve architectures that use AWS solutions.

84h.

Entering the world of FinOps and cost optimization in AWS. FinOps is a framework for managing operational costs in the cloud, bringing together the areas of finance and operations.
FinOps is a management practice that seeks to optimize cloud computing costs by using tools and best practices to help IT, finance, and business teams transfer financial responsibility to the cloud's variable spending model.

This includes a methodology to align the focus of organizations, a set of best practices for use in the cloud, a global community for sharing resources, and a series of tools to help cloud professionals address financial issues.

• What is FinOps? Why FinOps?
• Basic principles of FinOps. The core FinOps team.
• Differences between FinOps and cost optimizations.
• FinOps for everyday situations. The FinOps life-cycle. Inform, optimize, and operate.
• Train for ‘Practitioner’ certification.
• Cost savings aimed at AWS:
  • Practice Cloud Financial Management
  • Expenditure and usage awareness
  • Tag Policies
  • Cost and Usage Governance
  • Cost and Usage Analysis
  • Cost Visualization (Cost Explorer)
  • Cost and Usage Governance - Controls
  • Automated CUR Updates and Ingestion
  • Cost and Usage Analysis - SQL
  • Cost Visualization - QuickSight
  • Workload Efficiency
  • Automated Athena CUR Query and Email Delivery
  • Cost Categories
  • Cost Estimation
  • Cost Journey
  • Goals and Targets
  • Analyzing Licensing Costs
  • Splitting the CUR and Sharing Access
  • Cost effective resources
  • Pricing Models - Part 1
  • Pricing Model Analysis
  • Pricing Models - Part 2
  • Rightsizing Recommendations
  • Rightsizing with Compute Optimizer
  • Cost Anomaly Detection
  • Amazon S3 Intelligent Tiering
  • Manage demand and supply of resources
  • EC2 Scheduling at Scale
  • Optimize over time
  • Tools: AWS CUDOS and KubeCost + Grafana
  • Feed back

32h.

The Cloud SRE module is designed to prepare students for undertaking entry-level DevOps, cloud operations and support. It is also aimed at helping them prepare for the AWS SysOps Administrator - Associate exam. This module underlines AWS cloud best practices and recommended design patterns. Students are shown how to create automatable and repeatable deployments of networks and systems on the AWS platform. It covers AWS-specific features and tools related to configuration, deployment, and deployment.

• You will understand AWS infrastructure as it relates to system operations, such as global infrastructure, core services, and account security.
• Use the AWS Command Line Interface (AWS CLI) and understand complementary development and management tools.
• Manage, secure, and scale compute instances on AWS
• Manage, protect, and escalate configurations.
• Identify the container services and AWS services available for serverless computing.Manage, secure, and scale databases on AWS.
• Manage, secure, and scale databases on AWS.
• Creating a virtual private network using Amazon Virtual Private Cloud (Amazon VPC).
• Configure and manage storage options using storage services offered with AWS.
• Monitor the health of your infrastructure with services such as Amazon CloudWatch, AWS.
• CloudTrail y AWS Config
• Manage resource consumption on an AWS account using tags, Amazon CloudWatch, and AWS Trusted Advisor.
• Create and configure automated, repeatable deployments with tools such as Amazon Machine Images (AMIs) and AWS CloudFormation.

80h.

This module delivers the skills and knowledge that students will need to balance the requirements of the software development lifecycle, from programming and deployment to maintenance and upgrades.

• Revision of cloud computing services and models.
• Description of AWS development.
• Writing code that interacts with Amazon S3 using AWS SDKs
• Explanation of AWS IAM.
• Writing code that interacts with Amazon DynamoDB using AWS SDKs.
• Explanation of caching with Amazon CloudFront and Amazon ElastiCache.
• Configuring containers.
• Developing solutions with SQS and SNS.
• Writing code that interacts with AWS Lambda using AWS SDKs
• Creating a REST API using Amazon API Gateway.
• Description of the use of AWS Step Functions.
• How to create secure applications.
• Identifying best practices for deploying applications.

80h.

This module is designed for students to learn and get hands-on experience with the tasks, tools, and strategies used to collect, store, prepare, analyze, and visualize data for use in analytics and machine learning (ML) applications. Throughout the module, students will explore real-world application use cases, enabling them to make informed decisions as they build data pipelines for their specific applications.

• Summary of the role and value of data science in a data-driven organization.
• Recognizing how data elements influence decisions about the infrastructure of a data pipeline.
• Illustrating a data pipeline using AWS services to satisfy a generalized use case.
• Identifying risks and approaches to protect and control data at every step and transition of the data pipeline.
• Identifying scaling issues and best practices for creating pipelines that handle largescale data sets.
• Designing and building a data collection process with regard to constraints such as scalability, cost, fault tolerance, and latency.
• Selecting data storage options that meet the requirements and limitations of a given data analytics use case.
• Implementation of the steps required to process structured, semi-structured, and unstructured data formats in a data pipeline created using AWS.
• Explanation of the concept of MapReduce and how Amazon EMR is used in big data pipelines.
• How to differentiate the characteristics of an ML pipeline and its specific processing steps.
• Analyzing data using the appropriate AWS tools for a given use case.
• Implementing a data visualization solution aligned with an audience and data type.

80h.

Real world use cases where students can put what they have learned on the course into practice.

48h.

Apply all the knowledge acquired throughout the Master’s course in your Capstone Project. Carry out a complete project with NTTData, dealing with the problems and circumstances of your own clients, and present the results to a panel of experts.

• Definition of the idea with an assigned professional NTTData tutor.
• Selecting the project aim.
• Methodology approach.
• Use of market tools.
• Presentation before a jury of experts.

60h.