Live Course Module: Microsoft Azure cloud for Data Science

Total Duration: 35 Hours (5 Weeks)

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Week 1: Introduction to Azure & Data Science Essentials (6 Hours)

1. Introduction to Cloud Computing & Azure (2 Hrs)

   • Basics of cloud models (IaaS, PaaS, SaaS)

   • Microsoft Azure overview

   • Azure services relevant to data science

2. Setting Up Azure Environment (2 Hrs)

   • Creating a free Azure account

   • Navigating Azure Portal

   • Resource groups, subscriptions & cost management

3. Azure Data Science Workflow Overview (2 Hrs)

   • Data lifecycle in Azure

   • End-to-end machine learning pipeline

   • Introduction to Azure Machine Learning Studio

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Week 2: Data Storage & Processing in Azure (6 Hours)

4. Azure Storage Solutions (2 Hrs)

   • Azure Blob Storage

   • Data Lake Storage Gen2

   • Working with structured/unstructured data

5. Azure Databases & Querying (2 Hrs)

   • Azure SQL Database basics

   • Cosmos DB overview

   • Running SQL queries on Azure

6. Big Data Processing with Azure (2 Hrs)

   • Introduction to Azure Synapse Analytics

   • Using Apache Spark in Azure Synapse

   • Data ingestion with Azure Data Factory

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Week 3: Machine Learning with Azure (7 Hours)

7. Azure Machine Learning Studio Basics (2 Hrs)

   • Workspaces, datasets, experiments

   • Drag-and-drop ML model building

8. Model Training & Deployment (3 Hrs)

   • Supervised & unsupervised ML models

   • Model training with AutoML

   • Model deployment as web services

9. Python & Jupyter in Azure ML (2 Hrs)

   • Integrating Python scripts in Azure ML

   • Using Azure Notebooks & JupyterHub

   • Custom model training with Scikit-learn & PyTorch

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Week 4: Advanced Data Science & AI on Azure (7 Hours)

10. Deep Learning with Azure (2 Hrs)

    • Introduction to Azure GPU VMs

    • TensorFlow & PyTorch on Azure

    • Training neural networks

11. AI & Cognitive Services (2 Hrs)

    • Azure Cognitive Services overview

    • Computer Vision, Text Analytics, Speech APIs

    • Use cases in real-world projects

12. MLOps with Azure (3 Hrs)

    • Continuous integration/continuous deployment (CI/CD) for ML

    • Azure DevOps & ML pipelines

    • Model monitoring & retraining

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Week 5: Capstone Project & Certification Preparation (6–8 Hours)

13. End-to-End Data Science Project on Azure (4–5 Hrs)

    • Data ingestion (Azure Data Factory)

    • Data storage & cleaning (Azure Data Lake, SQL)

    • ML model training & deployment (Azure ML Studio)

    • Deploying final solution

14. Best Practices & Cost Optimization (1–2 Hrs)

    • Managing resources efficiently

    • Security & compliance in Azure

    • Scaling ML solutions

15. Exam Prep & Career Guidance (1 Hr)

    • Azure AI Fundamentals & Azure Data Scientist Associate overview

    • Mock Q&A session

    • Career roadmap for data science on Azure

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Learning Outcomes

• Understand Microsoft Azure ecosystem for data science.

• Build, train, and deploy ML models using Azure ML Studio.

• Work with big data using Azure Synapse & Data Factory.

• Implement AI solutions with Azure Cognitive Services.

• Manage MLOps pipelines for production-grade ML solutions.

• Complete an end-to-end Capstone Data Science Project on Azure.