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Course Outline

Day 1

Foundations of Data Products & Strategy Introduction to Modern Data Products Distinguishing Data Products from Traditional Data Systems Leveraging Data as a Strategic Business Asset Core Elements of a Data Product Ecosystem Identifying Suitable Business Problems for Data Products Overview of the Data Product Lifecycle (Ideation to Scaling) Industry Case Studies: Successful Data Products

Day 2

Data Product Design & Architecture Principles of Effective Data Product Design Understanding User Personas and Data Consumers Data Architecture Models (Centralised 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 (AWS / Azure / GCP Overview)

Day 3

Data Engineering & Implementation Data Ingestion Techniques (Batch vs. Streaming) ETL versus ELT Frameworks Constructing Robust Data Pipelines Data Storage Solutions (Data Lakes, Warehouses, Lakehouse) Tools for Data Transformation and Orchestration Introduction to Real-Time Data Processing Hands-on Lab: Developing a Simple Data Pipeline

Day 4

Analytics, AI Integration & Governance Integrating Analytics into Data Products Dashboards, KPIs, and Decision Intelligence Introduction to AI/ML within 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 Optimisation & Scaling Managing the Data Product Lifecycle within Organisations Monetisation Strategies for Data Products Future Trends: Generative AI & Autonomous Data Products Capstone Project Presentation & Feedback Session

Requirements

  • A foundational grasp of data principles and business reporting is advised.
  • Proficiency with Excel or similar basic data analysis tools is advantageous.
  • Understanding the role of data in business decision-making is highly beneficial.
  • Advanced programming expertise or technical backgrounds are not mandatory.
  • A genuine passion for data, analytics, and digital product development is essential.
 35 Hours

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