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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 (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: Constructing a Simple 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
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 other basic data analysis tools is advantageous.
  • An awareness of how data informs business decision-making will be beneficial.
  • No advanced programming or technical background is necessary.
  • A genuine interest in data, analytics, and digital product development is essential.
 35 Hours

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