Course Outline
Day 1
Foundational Concepts of Data Products & Strategy
Overview of Contemporary Data Products
Contrasting Data Products with Legacy Data Systems
Viewing Data as a Strategic Corporate Asset
Essential Elements of a Data Product Ecosystem
Identifying Business Challenges Amenable to Data Product Solutions
Summary of the Data Product Lifecycle (from Concept to Scale)
Case Studies: Precedents of Successful Data Products in Various Sectors
Day 2
Designing Data Products & Architectural Frameworks
Core Principles of Data Product Design
Comprehending User Personas and Data Stakeholders
Data Architecture Patterns (Centralized vs Data Mesh vs Hybrid Models)
Creating Scalable Data Pipelines
Data Modeling for Analytical and Operational Applications
APIs and Data Accessibility Layers
Cloud Infrastructure for Data Products (Overview of AWS / Azure / GCP)
Day 3
Data Engineering & Execution
Data Ingestion Techniques (Batch vs Streaming)
Comparison of ETL and ELT Frameworks
Constructing Dependable Data Pipelines
Data Storage Options (Data Lakes, Warehouses, Lakehouse)
Data Transformation and Orchestration Utilities
Basics of Real-Time Data Processing
Practical 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 within Data Products
Recommendation Engines and Predictive Modeling
Data Quality Control and Surveillance
Data Governance, Privacy, and Compliance (Overview of GDPR Principles)
Safeguarding Trust, Security & Reliability in Data Products
Day 5
Deployment, Scaling & Commercialization
Commercializing Data Solutions for End Users
Deployment Tactics and CI/CD for Data Products
Monitoring, Performance Tuning & Scaling
Managing the Data Product Lifecycle within Enterprises
Revenue Models for Data Products
Future Outlook: Generative AI & Autonomous Data Products
Capstone Project Showcase & Feedback Session
Requirements
- A foundational grasp of data principles and corporate reporting is advised.
- Proficiency with Excel or fundamental data analysis software is advantageous.
- An awareness of how data underpins business decision-making is beneficial.
- No advanced programming skills or deep technical background are necessary.
- A genuine interest in data, analytics, and digital product creation 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.
