Advanced Data Science Techniques With AWS Integration Course Syllabus

Overview: This course is designed for advanced learners aiming to master data science techniques integrated with AWS cloud services. Through a combination of hands-on labs, real-world case studies, and guided projects, learners will build scalable data pipelines, apply advanced analytics, and deploy machine learning models in cloud environments. The course spans approximately 18 hours, with each module combining conceptual learning and practical implementation, culminating in a final project that demonstrates end-to-end data science proficiency on AWS.

Module 1: Data Exploration & Preprocessing

Estimated time: 2 hours

• Introduction to key concepts in data exploration & preprocessing
• Review of tools and frameworks commonly used in practice
• Hands-on exercises applying data exploration & preprocessing techniques

Module 2: Statistical Analysis & Probability

Estimated time: 4 hours

• Hands-on exercises applying statistical analysis & probability techniques
• Discussion of best practices and industry standards
• Quiz and peer-reviewed assignment

Module 3: Machine Learning Fundamentals

Estimated time: 3 hours

• Introduction to key concepts in machine learning fundamentals
• Review of tools and frameworks commonly used in practice
• Discussion of best practices and industry standards
• Guided project work with instructor feedback

Module 4: Model Evaluation & Optimization

Estimated time: 2 hours

• Hands-on exercises applying model evaluation & optimization techniques
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Guided project work with instructor feedback

Module 5: Data Visualization & Storytelling

Estimated time: 3 hours

• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback
• Discussion of best practices and industry standards

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 4 hours

• Hands-on exercises applying advanced analytics & feature engineering techniques
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice
• Discussion of best practices and industry standards

Prerequisites

• Basic knowledge of data science concepts and workflows
• Familiarity with cloud computing fundamentals
• Experience with Python and machine learning libraries

What You'll Be Able to Do After

• Create data visualizations that communicate findings effectively
• Implement data preprocessing and feature engineering techniques
• Build and evaluate machine learning models using real-world datasets
• Work with large-scale datasets using industry-standard tools
• Design end-to-end data science pipelines for production environments