AI Data: Analyze, Govern, Plan Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to managing and utilizing data in AI-driven environments, focusing on analysis, governance, and strategic planning. Designed for beginners, it combines foundational AI concepts with practical applications in real-world contexts. The curriculum spans approximately 15-20 hours of learning, featuring interactive labs, case studies, and hands-on projects to build essential data strategy skills.
Module 1: Foundations of Computing & Algorithms
Estimated time: 4 hours
- Introduction to key concepts in foundations of computing & algorithms
- Review of tools and frameworks commonly used in practice
- Interactive lab: Building practical solutions
- Case study analysis with real-world examples
Module 2: Neural Networks & Deep Learning
Estimated time: 3 hours
- Review of tools and frameworks commonly used in practice
- Hands-on exercises applying neural networks & deep learning techniques
- Case study analysis with real-world examples
- Interactive lab: Building practical solutions
Module 3: AI System Design & Architecture
Estimated time: 2 hours
- Review of tools and frameworks commonly used in practice
- Hands-on exercises applying AI system design & architecture techniques
- Guided project work with instructor feedback
Module 4: Natural Language Processing
Estimated time: 3 hours
- Introduction to key concepts in natural language processing
- Review of tools and frameworks commonly used in practice
- Guided project work with instructor feedback
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2 hours
- Guided project work with instructor feedback
- Interactive lab: Building practical solutions
- Assessment: Quiz and peer-reviewed assignment
Module 6: Deployment & Production Systems
Estimated time: 4 hours
- Introduction to key concepts in deployment & production systems
- Interactive lab: Building practical solutions
- Assessment: Quiz and peer-reviewed assignment
Prerequisites
- Familiarity with basic computing concepts
- Basic understanding of data structures and workflows
- Access to standard software tools for data analysis
What You'll Be Able to Do After
- Analyze datasets using foundational AI and computing principles
- Apply data governance strategies to ensure compliance and quality
- Design AI systems with scalable architecture
- Implement practical solutions in natural language processing and computer vision
- Deploy AI models into production environments with monitoring and evaluation