Course Outline
Day 1
Foundations of Data Products and Strategy
Introduction to Contemporary 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 and 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 and 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, 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 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 and Autonomous Data Products
Capstone Project Presentation and Feedback Session
Requirements
- A fundamental understanding of data concepts and business reporting is advised.
- Familiarity with Excel or other basic data analysis tools is advantageous.
- An awareness of how data facilitates business decision-making is beneficial.
- No advanced programming skills or technical background are necessary.
- A strong 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.
