Duration: 3 Weeks | Total Time: 30 Hours
Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertise
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
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
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 & Spar
- 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
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
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