Course Outline
Day 1
Foundations of Data Products and Strategy
Introduction to Modern Data Products
Differentiating Data Products from Traditional Data Systems
Positioning Data as a Strategic Business Asset
Core Components of a Data Product Ecosystem
Identifying Business Problems Suited for Data Products
Overview of the Data Product Lifecycle (from Ideation to Scaling)
Case Studies: Successful Data Products in the Industry
Day 2
Data Product Design and Architecture
Principles of Data Product Design
Understanding User Personas and Data Consumers
Data Architecture Models (Centralised vs Data Mesh vs Hybrid)
Designing Scalable Data Pipelines
Data Modelling for Analytics and Operational Use
APIs and Data Accessibility Layers
Cloud Infrastructure for Data Products (Overview of AWS, Azure, and GCP)
Day 3
Data Engineering and Implementation
Data Ingestion Methods (Batch vs Streaming)
ETL vs ELT Frameworks
Constructing Reliable Data Pipelines
Data Storage Solutions (Data Lakes, Warehouses, and Lakehouse)
Data Transformation and Orchestration Tools
Introduction to Real-Time Data Processing
Practical Lab: Building a Simple Data Pipeline
Day 4
Analytics, AI Integration and 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 and Productisation
Productising Data Solutions for End Users
Deployment Strategies and CI/CD for Data Products
Monitoring, Performance Optimisation and Scaling
Data Product Lifecycle Management in Organisations
Monetisation Strategies for Data Products
Future Trends: Generative AI and Autonomous Data Products
Capstone Project Presentation and Feedback Session
Requirements
- A fundamental understanding of data concepts and business reporting is recommended.
- Familiarity with Excel or other basic data analysis tools is advantageous.
- Knowledge of how data informs business decision-making is beneficial.
- No advanced programming or technical background is required.
- 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.