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Course Outline

Day 1 

 Data Product Foundations & Strategy
Introduction to Contemporary Data Products
Data Products Compared to Traditional Data Systems
Treating Data as a Strategic Business Asset
Core Elements of a Data Product Ecosystem
Identifying Business Challenges Well-Suited for Data Products
Overview of the Data Product Lifecycle (from Ideation to Scaling)
Case Studies: Notable Data Products in Industry

Day 2 

 Data Product Design & Architecture
Fundamentals 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 Uses
APIs and Data Accessibility Layers
Cloud Infrastructure for Data Products (Overview of AWS / Azure / GCP)

Day 3

Data Engineering & Implementation Data Ingestion Techniques (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
Practical 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 into End-User Products
Deployment Strategies and CI/CD for Data Products
Monitoring, Performance Optimization & Scaling
Data Product Lifecycle Management Within 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.
  • Familiarity with Excel or similar entry-level data analysis tools is advantageous.
  • Insight into how data informs business decision-making will be beneficial.
  • No advanced programming or technical background is necessary.
  • A strong interest in data, analytics, and digital product development is essential.
 35 Hours

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