Course Outline
Day 1
Foundations of Data Products & Strategy
Introduction to Contemporary Data Products
Distinctions Between Data Products and Traditional Data Systems
Recognizing Data as a Strategic Business Asset
Core Elements of a Data Product Ecosystem
Identifying Business Challenges Suited for Data Products
Overview of the Data Product Lifecycle (From Ideation to Scaling)
Case Studies: Notable Data Products in the Industry
Day 2
Data Product Design & Architecture
Core Principles of Data Product Design
Understanding User Personas and Data Consumers
Data Architecture Models (Centralized vs. Data Mesh vs. Hybrid)
Architecting 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 Techniques (Batch vs. Streaming)
ETL vs. ELT Frameworks
Constructing Robust Data Pipelines
Data Storage Solutions (Data Lakes, Warehouses, Lakehouse)
Data Transformation and Orchestration Tools
Introduction to Real-Time Data Processing
Hands-on Lab: Constructing a Basic 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 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 & Productization
Refining 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 grasp of data concepts and business reporting is advised.
- Knowledge of Excel or comparable basic data analysis tools is advantageous.
- Understanding the role of data in business decision-making adds value.
- 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.