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Course Outline

Day 1

Foundations of Data Products & Strategy Introduction to Modern Data Products Data Products Versus Traditional Data Systems Data as a Strategic Business Asset Core Components of a Data Product Ecosystem Identifying Business Challenges Amenable to Data Products Overview of the Data Product Lifecycle (Ideation to Scaling) Case Studies: Successful Industry Implementations

Day 2

Data Product Design & Architecture Core 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 Cases 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 Versus 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 Integrating Analytics into Data Products Dashboards, KPIs, and Decision Intelligence Introduction to AI/ML Applications 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 within Organizations Monetization Strategies for Data Products Future Trends: Generative AI and Autonomous Data Products Capstone Project Presentation and Feedback Session

Requirements

  • A foundational grasp of data concepts and business reporting is advisable.
  • Proficiency in Excel or similar basic data analysis tools is advantageous.
  • Familiarity with the role of data in supporting business decision-making is beneficial.
  • No advanced programming skills or technical background are necessary.
  • A genuine passion for data, analytics, and digital product development is essential.
 35 Hours

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