Cloud-based Data Science (aws, Azure, Gcp) Training Course: Scalable Data Workflows

Introduction

Revolutionize your data science projects with our Cloud-Based Data Science (AWS, Azure, GCP) Training Course. This program is designed to equip you with the essential skills to utilize cloud platforms for scalable data science workflows, enabling you to build and deploy robust data solutions in the cloud. In today's data-intensive environment, mastering cloud-based data science is crucial for handling large datasets, deploying machine learning models, and scaling your data science projects efficiently. Our cloud data science training course offers hands-on experience and expert guidance, empowering you to leverage the power of AWS, Azure, and GCP.

This scalable data workflows training delves into the core concepts of cloud-based data science, covering topics such as cloud storage, data processing, machine learning services, and deployment strategies. You'll gain expertise in using industry-leading cloud platforms to utilize cloud platforms for scalable data science workflows, meeting the demands of modern data science projects. Whether you're a data scientist, machine learning engineer, or cloud professional, this Cloud-Based Data Science (AWS, Azure, GCP) course will empower you to build and deploy high-performance cloud-based data solutions.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• Cloud Professionals
• Data Engineers
• Data Architects
• Software Developers
• Anyone needing cloud-based data science skills

Course Objectives:

• Understand the fundamentals of cloud-based data science (AWS, Azure, GCP).
• Master cloud storage solutions for data science (S3, Blob Storage, Cloud Storage).
• Utilize cloud-based data processing services (EMR, Databricks, Dataflow).
• Implement machine learning services on cloud platforms (SageMaker, Azure ML, Vertex AI).
• Design and build scalable data science workflows in the cloud.
• Optimize cloud resources for cost-effectiveness and performance.
• Troubleshoot and address common cloud-based data science challenges.
• Implement data pipeline and model deployment strategies in the cloud.
• Integrate cloud-based data science with real-world applications.
• Understand how to handle security and compliance in cloud environments.
• Explore advanced cloud-based data science techniques (e.g., serverless data processing, containerized ML).
• Apply real world use cases for cloud-based data science.
• Leverage cloud-specific libraries and tools for efficient development.

Duration

10 Days

Course content

Module 1: Introduction to Cloud-Based Data Science (AWS, Azure, GCP)

• Fundamentals of cloud-based data science (AWS, Azure, GCP).
• Overview of cloud storage, data processing, and ML services.
• Setting up cloud environments for data science projects.
• Introduction to cloud-specific libraries and tools.
• Best practices for cloud-based data science.

Module 2: Cloud Storage Solutions

• Implementing cloud storage solutions (S3, Blob Storage, Cloud Storage).
• Utilizing data lake architectures for large datasets.
• Designing and building data storage pipelines.
• Optimizing storage for data retrieval and processing.
• Best practices for cloud storage.

Module 3: Cloud-Based Data Processing Services

• Implementing cloud-based data processing services (EMR, Databricks, Dataflow).
• Utilizing distributed computing for large-scale data processing.
• Designing and building data processing pipelines.
• Optimizing processing for performance and cost.
• Best practices for data processing.

Module 4: Machine Learning Services on Cloud Platforms

• Implementing machine learning services (SageMaker, Azure ML, Vertex AI).
• Utilizing pre-built ML models and custom ML pipelines.
• Designing and building ML training and deployment systems.
• Optimizing ML models for cloud deployment.
• Best practices for ML services.

Module 5: Scalable Data Science Workflows

• Designing and building scalable data science workflows in the cloud.
• Implementing automated data pipelines and ML deployments.
• Utilizing workflow orchestration tools (Airflow, Step Functions).
• Optimizing workflows for automation and efficiency.
• Best practices for scalable workflows.

Module 6: Cloud Resource Optimization

• Optimizing cloud resources for cost-effectiveness and performance.
• Utilizing auto-scaling and resource management tools.
• Implementing cost monitoring and budgeting strategies.
• Designing efficient resource allocation plans.
• Best practices for resource optimization.

Module 7: Troubleshooting Cloud-Based Data Science Challenges

• Debugging common cloud-based data science issues.
• Analyzing cloud service performance and errors.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common cloud challenges.
• Best practices for troubleshooting.

Module 8: Data Pipeline and Model Deployment

• Implementing data pipeline and model deployment strategies.
• Utilizing containerization and serverless deployments.
• Designing and building deployment pipelines.
• Optimizing deployments for production environments.
• Best practices for deployment.

Module 9: Integration with Real-World Applications

• Integrating cloud-based data science with real-world applications.
• Utilizing cloud APIs and data connectors.
• Implementing real-time cloud data processing.
• Optimizing integration for business impact.
• Best practices for integration.

Module 10: Security and Compliance in Cloud Environments

• Implementing security and compliance in cloud environments.
• Utilizing IAM and access control policies.
• Designing and building secure cloud data pipelines.
• Optimizing data handling for regulatory compliance.
• Best practices for security.

Module 11: Advanced Cloud-Based Data Science Techniques

• Implementing serverless data processing and ETL.
• Utilizing containerized machine learning deployments.
• Designing and building advanced cloud data science solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing cloud-based data science for e-commerce personalization.
• Utilizing cloud-based data science for financial risk analysis.
• Implementing cloud-based data science for healthcare analytics.
• Utilizing cloud-based data science for IoT data processing.
• Best practices for real-world applications.

Module 13: Cloud-Specific Libraries and Tools Implementation

• Utilizing Boto3 (AWS), Azure SDK, and Google Cloud Client Libraries.
• Implementing cloud-based data pipelines with cloud-specific tools.
• Designing and building solutions with cloud-specific libraries.
• Optimizing library usage for efficient development.
• Best practices for tool implementation.

Module 14: Monitoring and Observability for Cloud Data Science

• Implementing monitoring and observability for cloud data science.
• Utilizing cloud-native monitoring tools (CloudWatch, Azure Monitor, Stackdriver).
• Designing and building monitoring dashboards.
• Optimizing monitoring for proactive issue detection.
• Best practices for monitoring.

Module 15: Future Trends in Cloud-Based Data Science

• Emerging trends in cloud-based data science.
• Utilizing AI for cloud data management and automation.
• Implementing federated learning and distributed ML in the cloud.
• Best practices for future cloud applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.