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Course Outline

Day 1 

 Foundations of Data Products & Strategy
Introduction to Modern Data Products
Data Products vs Traditional Data Systems
Data as a Strategic Business Asset
Key Components of a Data Product Ecosystem
Identifying Business Problems Suitable for Data Products
Data Product Lifecycle Overview (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 (AWS / Azure / GCP overview)

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 (GDPR concepts overview)
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

Requirements

  • A foundational understanding of data concepts and business reporting is recommended.
  • Familiarity with Excel or basic data analysis tools is advantageous.
  • Knowledge of how data informs business decision-making is beneficial.
  • Advanced programming or technical expertise is not required.
  • An enthusiasm for data, analytics, and digital product development is essential.
 35 Hours

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