Course Outline
Day 1
Fundamentals of Data Products & Strategy
Overview of Modern Data Products
Distinguishing Data Products from Traditional Data Systems
Treating Data as a Strategic Business Asset
Core Elements of a Data Product Ecosystem
Identifying Business Challenges Amenable to Data Product Solutions
Overview of the Data Product Lifecycle (from Ideation to Scale)
Industry Case Studies: Successful Data Product Implementations
Day 2
Data Product Design & Architecture
Core Principles of Data Product Design
Analyzing User Personas and Data Consumers
Exploring Data Architecture Models (Centralized vs. Data Mesh vs. Hybrid)
Crafting Scalable Data Pipelines
Data Modeling for Both Analytics and Operational Purposes
APIs and Data Accessibility Layers
Overview of Cloud Infrastructure for Data Products (AWS / Azure / GCP)
Day 3
Data Engineering & Execution
Data Ingestion Strategies (Batch vs. Streaming)
Comparing ETL and ELT Frameworks
Constructing Resilient Data Pipelines
Data Storage Options (Data Lakes, Warehouses, Lakehouse)
Data Transformation and Orchestration Tools
Introduction to Real-Time Data Processing
Practical Lab: Developing a Basic Data Pipeline
Day 4
Analytics, AI Integration & Governance
Integrating Analytics into Data Products
Dashboards, KPIs, and Decision Intelligence
Introducing AI/ML in Data Products
Recommendation Systems and Predictive Modeling
Data Quality Management and Monitoring
Data Governance, Privacy, and Compliance (Overview of GDPR concepts)
Safeguarding Trust, Security, and Reliability in Data Products
Day 5
Deployment, Scaling & Productization
Transforming Data Solutions into User-Facing Products
Deployment Strategies and CI/CD for Data Products
Monitoring, Performance Tuning, and Scaling
Data Product Lifecycle Management in Corporate Environments
Revenue Models and Monetization Strategies for Data Products
Emerging Trends: Generative AI & Autonomous Data Products
Capstone Project Presentation & Review Session
Requirements
- A foundational grasp of data concepts and business reporting is advised.
- Experience with Excel or similar basic data analysis tools is advantageous.
- Understanding how data informs business decision-making will be beneficial.
- No advanced programming skills or deep technical background are necessary.
- A genuine interest in data, analytics, and digital product development is required.
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.
