Struttura del corso
Day 1
Foundations of Data Products & Strategy
Introduction to Modern Data Products
Distinguishing Data Products from Traditional Data Systems
Positioning Data as a Strategic Business Asset
Core Components of a Data Product Ecosystem
Identifying Business Problems Suitable for Data Products
Overview of the Data Product Lifecycle (Ideation to Scaling)
Case Studies: Successful Data Products in Industry
Day 2
Data Product Design & 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 & 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 & 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 & 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 & Scaling
Data Product Lifecycle Management in Organizations
Monetization Strategies for Data Products
Future Trends: Generative AI & Autonomous Data Products
Capstone Project Presentation & Feedback Session
Requisiti
- Si consiglia una conoscenza di base dei concetti dati e della reportistica aziendale.
- La familiarità con Excel o altri strumenti base di analisi dati è utile.
- È vantaggiosa la consapevolezza del ruolo dei dati nel supporto alle decisioni aziendali.
- Non è richiesto un background avanzato in programmazione o in ambito tecnico.
- È fondamentale un interesse verso i dati, l'analisi e lo sviluppo di prodotti digitali.
Recensioni (2)
La varietà delle informazioni condivise e la chiarezza nel spiegare i termini in linguaggio semplice.
Arisbe Mendoza - Fairtrade International
Corso - GDPR Workshop
Traduzione automatica
È una sessione pratica.
Vorraluck Sarechuer - Total Access Communication Public Company Limited (dtac)
Corso - Talend Open Studio for ESB
Traduzione automatica
