Course Outline
Day 1: Foundations of Data Products & Strategy
• Introduction to Modern Data Products
• Data Products Compared to Traditional Data Systems
• Data as a Strategic Business Asset
• Key Components of a Data Product Ecosystem
• Identifying Business Problems Suitable for Data Products
• Overview of the Data Product Lifecycle (from Ideation to Scaling)
• Case Studies: Successful Data Products in Industry
Day 2: Data Product Design & Architecture
• Principles of Data Product Design
• Understanding User Personas and Data Consumers
• Data Architecture Models (Centralized vs. Data Mesh vs. Hybrid)
• Designing Scalable Data Pipelines
• Data Modeling for Analytics and Operational Use
• APIs and Data Accessibility Layers
• Cloud Infrastructure for Data Products (Overview of AWS, Azure, GCP)
Day 3: Data Engineering & Implementation
• Data Ingestion Methods (Batch vs. Streaming)
• ETL vs. ELT Frameworks
• Building Reliable Data Pipelines
• Data Storage Solutions (Data Lakes, Warehouses, Lakehouse)
• Data Transformation and Orchestration Tools
• Introduction to Real-Time Data Processing
• Hands-on Lab: Building a Simple Data Pipeline
Day 4: Analytics, AI Integration & Governance
• Embedding Analytics into Data Products
• Dashboards, KPIs, and Decision Intelligence
• Introduction to AI/ML in Data Products
• Recommendation Systems and Predictive Models
• Data Quality Management and Monitoring
• Data Governance, Privacy, and Compliance (Overview of GDPR concepts)
• Ensuring Trust, Security, and Reliability in Data Products
Day 5: Deployment, Scaling & Productization
• Productizing Data Solutions for End Users
• Deployment Strategies and CI/CD for Data Products
• Monitoring, Performance Optimization, and Scaling
• Data Product Lifecycle Management in Organizations
• Monetization Strategies for Data Products
• Future Trends: Generative AI & Autonomous Data Products
• Capstone Project Presentation & Feedback Session
Requirements
- Recommended: A foundational understanding of data concepts and business reporting.
- Helpful: Familiarity with Excel or basic data analysis tools.
- Beneficial: Awareness of the role data plays in business decision-making.
- Not required: Advanced programming skills or a technical background.
- Essential: A genuine interest in data, analytics, and digital product development.
Testimonials (2)
The variety of the information shared and the clarity to explain terms in plain English.
Arisbe Mendoza - Fairtrade International
Course - GDPR Workshop
It's a hands-on session.