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Course Outline

Day 1 

• Foundations of Data Products & Strategy
• Introduction to Modern Data Products
• Differentiating Data Products from Traditional Data Systems
• Leveraging Data as a Strategic Business Asset
• Core Components of a Data Product Ecosystem
‣ Identifying Business Challenges Suitable 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 Effective 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 Methods (Batch vs. Streaming)
• ETL vs. ELT Frameworks
• Constructing 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

Requirements

  • A foundational grasp of data concepts and business reporting is advised.
  • Comfort with Excel or similar basic data analysis tools is advantageous.
  • Understanding how data underpins business decision-making will be beneficial.
  • Advanced programming or technical expertise is not required.
  • A genuine interest in data, analytics, and digital product development is essential.
 35 Hours

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