Course Outline
Day 1
† Foundations of Data Products & Strategy
† Introduction to Modern Data Products
† Data Products vs 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 (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 & 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 & 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
- A foundational understanding of data concepts and business reporting is advised.
- Basic familiarity with Excel or other fundamental data analysis tools is advantageous.
- An awareness of how data informs business decision-making will be beneficial.
- No advanced programming skills or technical background are necessary.
- A genuine interest in data, analytics, and digital product development is essential.
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.
