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Course Outline

Day 1: Foundations of Data Products & Strategy

• Introduction to Modern Data Products
• Data Products Compared to Traditional Data Systems
• Data as a Strategic Business Asset
• Key Components of a Data Product Ecosystem
• Identifying Business Problems Suitable for Data Products
• Overview of the Data Product Lifecycle (from 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, and 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, and 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

  • Recommended: A foundational understanding of data concepts and business reporting.
  • Helpful: Familiarity with Excel or basic data analysis tools.
  • Beneficial: Awareness of the role data plays in business decision-making.
  • Not required: Advanced programming skills or a technical background.
  • Essential: A genuine interest in data, analytics, and digital product development.
 35 Hours

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