AWS Cloud9 for Data Science Training Course

This instructor-led, live training in US (online or onsite) is aimed at data scientists who wish to utilize the Anaconda ecosystem to manage and deploy packages and data analysis workflows within a single platform for government. 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 for government use. - Manage packages, environments, and channels using Anaconda Navigator. - Utilize Conda, R, and Python packages for data science and machine learning tasks. - Explore practical use cases and techniques for managing multiple data environments in a public sector context.

This instructor-led, live training in [location] (onsite or remote) is designed for engineers who wish to deploy and manage IoT devices on AWS for government use. By the end of this training, participants will be able to construct an IoT platform that encompasses the deployment and management of a backend, gateway, and devices utilizing AWS services.

This instructor-led, live training in 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 for government use.

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, ensuring alignment with public sector workflows and governance.

This instructor-led, live training in [location] (onsite or remote) is aimed at developers who wish to use AWS Lambda to build and deploy services and applications to the cloud without needing to provision the execution environment (servers, VMs, containers, availability, scalability, storage, etc.). By the end of this training, participants will be able to: - Configure AWS Lambda to execute a function. - Understand FaaS (Functions as a Service) and the advantages of serverless development for government applications. - Build, upload, and execute AWS Lambda functions. - Integrate Lambda functions with various event sources. - Package, deploy, monitor, and troubleshoot Lambda-based applications.

The unprecedented growth in information and data in today’s world is driving rapid innovation and pushing the boundaries of what is possible. The role of Data Scientist is one of the most in-demand skills across industries.

We offer more than just theoretical learning; we provide practical, marketable skills that bridge the gap between academia and industry demands.

This 7-week curriculum can be tailored to meet specific requirements for government or other sectors. For further information, please contact us or visit the Govtra Institute website.

Audience:

This program is designed for post-graduate level individuals as well as those with the necessary prerequisite skills, which will be determined through an assessment and interview process.

Delivery:

The course delivery will combine Instructor-Led Classroom sessions and Instructor-Led Online sessions. Typically, the first week will be classroom-led, weeks 2 to 6 will be virtual classroom sessions, and the final week will return to a classroom-led format.

This instructor-led, live training in [location] (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9 for government. By the end of this training, participants will be able to: - Set up and configure AWS Cloud9 to support DevOps workflows. - Implement continuous integration and continuous delivery (CI/CD) pipelines. - Automate testing, monitoring, and deployment processes using AWS Cloud9. - Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows for government. - Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda. By the end of this training, participants will be able to: - Understand the fundamentals of serverless architecture. - Set up AWS Cloud9 for developing serverless applications. - Develop, test, and deploy serverless applications using AWS Lambda. - Integrate AWS Lambda with other AWS services such as API Gateway and S3. - Optimize serverless applications for performance and cost efficiency, ensuring alignment with public sector workflows and governance standards for government.

Big data refers to datasets that are too large and complex for conventional data processing applications to manage effectively. Challenges associated with big data for government include the capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and ensuring the privacy of information. These issues require robust solutions that align with public sector workflows, governance, and accountability standards.

Summary:

• Fundamentals of Internet of Things (IoT) architecture and functionality for government applications.
• Understanding the components: "Things," sensors, internet connectivity, and their alignment with business functions in IoT systems.
• Overview of essential IoT software components, including hardware, firmware, middleware, cloud services, and mobile applications for government use.
• Key IoT functionalities such as fleet management, data visualization, Software-as-a-Service (SaaS) solutions for fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geofencing for government operations.
• Basics of IoT device communication with the cloud using MQTT protocol for government projects.
• Connecting IoT devices to AWS IoT Core using MQTT for secure and efficient data transmission in government settings.
• Integrating AWS IoT Core with AWS Lambda functions for computation and data storage for government applications.
• Connecting a Raspberry Pi to AWS IoT Core for simple data communication suitable for government projects.
• Managing alerts and events within the context of government operations.
• Sensor calibration procedures for maintaining accuracy in government IoT deployments.

Summary:

• Foundational concepts of Internet of Things (IoT) architecture and functions
• Overview of "things," sensors, internet connectivity, and their alignment with business functions in IoT systems
• Essential components of IoT software, including hardware, firmware, middleware, cloud services, and mobile applications
• Key IoT functionalities such as fleet management, data visualization, Software-as-a-Service (SaaS) solutions for fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geo-fencing
• Basic principles of IoT device communication with the cloud using Message Queuing Telemetry Transport (MQTT)
• Connecting IoT devices to AWS with MQTT through AWS IoT Core
• Integrating AWS IoT Core with AWS Lambda for computation and data storage using DynamoDB
• Establishing a connection between a Raspberry Pi and AWS IoT Core for simple data communication
• Practical exercises involving the use of a Raspberry Pi and AWS IoT Core to create a smart device
• Data visualization and communication through web interfaces, ensuring seamless integration and user-friendly access

This curriculum is designed to provide comprehensive training and practical experience for government professionals in leveraging IoT technologies to enhance operational efficiency and innovation.

This instructor-led, live training in US (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea." The training guides participants through the creation of a sample data science project using the Jupyter ecosystem. By the end of this training, participants will be able to: - Install and configure Jupyter, including setting up and integrating a team repository on Git. - Utilize Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration. - Create, share, and organize Jupyter Notebooks with team members. - Select from languages like Scala, Python, or R to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface. This training is designed to enhance teamwork and efficiency in data science projects for government agencies and other public sector organizations.

This instructor-led, live training in [location] (online or onsite) is aimed at data scientists and developers who wish to advance their careers in Data Science using Kaggle. By the end of this training, participants will be able to: - Gain a comprehensive understanding of data science and machine learning. - Explore various techniques in data analytics. - Learn about Kaggle and its functionalities, specifically tailored for government applications.

In the first part of this training, we cover the fundamentals of MATLAB and its role as both a language and a platform for government. This discussion includes an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning, and predictive analytics in a government context. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating data processing and report generation for government tasks.

Throughout the course, participants will apply the concepts learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough understanding of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation for government.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

• The course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.

Note

• Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements for government use cases, please contact us to arrange.

This instructor-led, live training in 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 for government applications. - Differentiate between Modin, Dask, and Ray in terms of their suitability for various data processing tasks. - Perform Pandas operations more efficiently using Modin. - Implement the entire Pandas API and functions to enhance data analysis capabilities.

This instructor-led, live training in [location] (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 and cuML. 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 for government applications. - Leverage GPUs to accelerate end-to-end data and analytics pipelines. - Implement GPU-accelerated data preparation and ETL processes using 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.