Introduction to Google Colab for Data Science Training Course

This instructor-led, live training in the US (online or onsite) is aimed at cloud engineers and developers who wish to use CloudFormation to manage infrastructure resources within the AWS ecosystem.

By the end of this training, participants will be able to:

• Implement CloudFormation templates to automate infrastructure management.
• Integrate existing AWS resources into CloudFormation.
• Use StackSets to manage stacks across multiple accounts and regions.

This instructor-led, live training in the US (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.

By the end of this training, participants will be able to:

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in the US (online or onsite) is aimed at developers who wish to integrate a DynamoDB NoSQL database into a web application hosted on AWS.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start integrating data into DynamoDB.
• Integrate DynamoDB into web applications and mobile applications.
• Move data in AWS with AWS services.
• Implement operations with AWS DAX.

This instructor-led, live training in the US (onsite or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in the US (online or onsite) is aimed at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in the US (online or onsite) is aimed at developers who wish to learn how to build microservices on Microsoft Azure Service Fabric (ASF).

By the end of this training, participants will be able to:

• Use ASF as a platform for building and managing microservices.
• Understand key microservices programming concepts and models.
• Create a cluster in Azure.
• Deploy microservices on premises or in the cloud.
• Debug and troubleshoot a live microservice application.

This instructor-led, live training in the US (online or onsite) is aimed at engineers who wish to use AWS CloudFormation to automate the process of managing AWS cloud infrastructure.

By the end of this training, participants will be able to:

• Enable AWS services to get started managing infrastructure.
• Understand and apply the principle of "infrastructure as code".
• Improve quality and lower the costs of deploying infrastructure.
• Write AWS CloudFormation Templates using YAML.

This instructor-led, live training in the US (online or onsite) is aimed at programmers and developers who wish to use Fn in creating serverless applications and services.

By the end of this training, participants will be able to:

• Set up Fn to create directories and functions.
• Create applications using different programming languages.
• Monitor functions to resolve issues at the development and deployment stages.

This instructor-led, live training in the US (online or onsite) is aimed at data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

By the end of this training, participants will be able to:

• Learn about data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

This instructor-led, live training in the US (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

By the end of this training, participants will be able to:

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

This instructor-led, live training in the US (onsite or remote) is aimed at developers who wish to use OpenFaas to create, build, test, debug and deploy event-driven functions without needing to worry about managing the underlying server infrastructure.

By the end of this training, participants will be able to:

• Install and configure OpenFaas.
• Package any binary or code as a serverless function without repetitive boiler-plate coding.
• Decouple from AWS Lambda to avoid lock-in.
• Deploy event-driven functions to an on-premise server or to the cloud.

This instructor-led, live training in the US (online or onsite) is aimed at software engineers who wish to develop parallel applications using OpenMP.

By the end of this training, participants will be able to:

• Understand and use parallel programming with Fortran in OpenMP.
• Calculate fractals in parallel to render multiple pixels and characters.
• Implement vector programming with SIMD extensions for HPC systems.
• Add parallel blocks for specifying shared memory parallelism.

This instructor-led, live training in the US (online or onsite) is aimed at data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.

By the end of this training, participants will be able to:

• Set up the necessary development environment to build data models with NVIDIA RAPIDS.
• Understand the features, components, and advantages of RAPIDS.
• Leverage GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
• Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis with cuXfilter and cuGraph.

This instructor-led, live training in the US (onsite or remote) is aimed at developers who wish to use Serverless Framework on AWS and other cloud platforms to create and deploy microservice applications.

By the end of this training, participants will be able to:

• Set up Serverless Framework to work with compute services such as AWS Lambda.
• Reduce the complexity and cost of deploying microservices on different cloud platforms.
• Emit and capture events and execute functions automatically.

This instructor-led, live training in the US (online or onsite) is aimed at developers and DevOps engineers who wish to utilize a serverless approach for building enterprise applications in Kubernetes.

By the end of this training, participants will be able to:

• Setup and configure the Kubernetes system to start developing with a serverless architecture.
• Understand the concepts and principles foundational to serverless environments.
• Operate toolchains necessary to serverless development and integrate it with Kubernetes components.
• Practice their skill in Python programming language and apply it to implement  serverless systems.
• Secure enterprise applications that are deployed through a serverless framework on Kubernetes.
• Utilize modern cloud computing methods in optimizing DevOps task processing workflows.