Live Course Module: AWS cloud for Data Science

Total Duration: 30 Hours (3 Weeks)

Week 1 – AWS Foundations & Data Handling (10 Hrs)

Topics Covered:

• Introduction to AWS & Cloud Basics

• IAM (Identity & Access Management)

• Data Storage with Amazon S3

• ETL & Data Preparation with AWS Glue

• Serverless SQL Queries with Amazon Athena

Outcome:

• Understand AWS cloud environment & security basics

• Store, manage, and secure datasets in S3

• Build simple ETL workflows with Glue

• Query structured/unstructured data using Athena

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Week 2 – Compute, Machine Learning & Visualization (10 Hrs)

Topics Covered:

• AWS EC2 setup for data science environment

• Amazon SageMaker (Notebooks, Training, Deployment)

• Model training & hyperparameter tuning

• Real-time and batch inference deployment

• Visualization & BI with Amazon QuickSight

Outcome:

• Build and manage compute environments (EC2, SageMaker)

• Train ML models using SageMaker

• Deploy models for real-time predictions

• Create dashboards and data visualizations with QuickSight

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Week 3 – Advanced Tools, MLOps & Project (10 Hrs)

Topics Covered:

• Big Data Analytics with EMR (Hadoop/Spark)

• Real-time data ingestion with AWS Kinesis

• MLOps using SageMaker Pipelines

• End-to-End Data Science Project (S3 → Glue → Athena → SageMaker → QuickSight)

• Cost Optimization, Security, and Best Practices

Outcome:

• Run large-scale data processing with EMR & Spark

• Stream real-time data using Kinesis

• Automate ML workflows with MLOps pipelines

• Complete a hands-on end-to-end AWS data science project

• Apply cost-saving and security strategies in AWS

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Final Outcomes of the Course

By the end of 30 hours (3 weeks), learners will be able to:
✅ Set up AWS environments for data science securely
✅ Store, clean, and process large datasets (batch & real-time)
✅ Train and deploy ML models using SageMaker
✅ Visualize insights with AWS QuickSight dashboards
✅ Design end-to-end AWS-based data science workflows