Course Outline
Day 1
Foundations of Data Products & Strategy
Introduction to Modern Data Products
Differentiating Data Products from Traditional Data Systems
Data as a Core Business Asset
Core Elements of the Data Product Ecosystem
Recognizing Business Challenges Suitable for Data Products
Overview of the Data Product Lifecycle (from Ideation to Scaling)
Case Studies: Industry Success Stories in Data Products
Day 2
Data Product Design & Architecture
Core Principles of Data Product Design
Analyzing User Personas and Data Consumers
Data Architecture Paradigms (Centralized vs. Data Mesh vs. Hybrid)
Architecting Scalable Data Pipelines
Data Modeling for Analytics and Operational Use Cases
APIs and Data Accessibility Layers
Cloud Infrastructure Options for Data Products (AWS, Azure, GCP Overview)
Day 3
Data Engineering & Implementation
Data Ingestion Techniques (Batch vs. Streaming)
ETL vs. ELT Frameworks
Constructing Robust Data Pipelines
Data Storage Architectures (Data Lakes, Warehouses, Lakehouses)
Data Transformation and Orchestration Utilities
Introduction to Real-Time Data Processing
Practical Lab: Developing a Simple Data Pipeline
Day 4
Analytics, AI Integration & Governance
Integrating Analytics into Data Products
Dashboards, KPIs, and Decision Intelligence
Introduction to AI/ML Applications in Data Products
Recommendation Systems and Predictive Modeling
Data Quality Management and Monitoring
Data Governance, Privacy, and Compliance (Overview of GDPR)
Establishing 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
Managing the Data Product Lifecycle within Organizations
Monetization Approaches for Data Products
Future Trends: Generative AI and Autonomous Data Products
Capstone Project Presentation & Feedback Session
Requirements
- A foundational understanding of data concepts and business reporting is advised.
- Previous exposure to Excel or comparable basic data analysis tools is advantageous.
- Familiarity with the role of data in supporting business decision-making will be beneficial.
- Advanced programming skills or a technical background are not mandatory.
- A genuine interest in data, analytics, and digital product development is crucial.
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.