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Course Outline

Day 1

• Foundations of Data Products & Strategy
• Introduction to Contemporary Data Products
• Data Products Compared to Traditional Data Systems
• 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: Successful Data Products in the 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)
• Architecting Scalable Data Pipelines
• Data Modeling for Analytics and Operational Use Cases
• 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
• Constructing Reliable Data Pipelines
• Data Storage Solutions (Data Lakes, Warehouses, Lakehouse)
• Tools for Data Transformation and Orchestration
• 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 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
• Transforming Data Solutions for End Users
• Deployment Strategies and CI/CD for Data Products
• Monitoring, Performance Optimization & Scaling
• Managing the Data Product Lifecycle within Organizations
• Monetization Strategies for Data Products
• Future Trends: Generative AI & Autonomous Data Products
• Capstone Project Presentation & Feedback Session

Requirements

  • A fundamental grasp of data concepts and business reporting is advised.
  • Familiarity with Excel or similar basic data analysis tools is advantageous.
  • Understanding how data underpins business decision-making is beneficial.
  • No advanced programming skills or technical background are necessary.
  • A genuine interest in data, analytics, and digital product development is essential.
 35 Hours

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