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Course Outline

Day 1

Foundations of Data Products and Strategy
Overview of Modern Data Product Frameworks
Differentiating Data Products from Conventional Systems
Leveraging Data as a Strategic Asset for Public Value
Core Elements of the Data Product Ecosystem
Evaluating Business Challenges Appropriate for Data Solutions
Data Product Lifecycle (From Concept to Scale)
Review of Industry Case Studies: Effective Data Products

Day 2

Design and Architecture of Data Products
Foundational Principles for Data Product Design
Analyzing User Personas and Data Consumer Needs
Data Architecture Paradigms (Centralized, Data Mesh, Hybrid)
Constructing Scalable Data Pipelines
Data Modeling for Analytical and Operational Workloads
Application Programming Interfaces (APIs) and Accessibility Layers
Cloud Infrastructure Overview for Data Solutions (AWS, Azure, GCP)

Day 3

Data Engineering and Implementation
Data Ingestion Methodologies (Batch Processing vs. Streaming)
ETL versus ELT Frameworks
Developing Resilient Data Pipelines
Data Storage Architectures (Data Lakes, Warehouses, Lakehouses)
Data Transformation and Orchestration Tools
Introduction to Real-Time Data Processing
Practical Application: Constructing a Basic Data Pipeline

Day 4

Analytics, Artificial Intelligence Integration, and Governance
Integrating Analytics into Data Products
Dashboard Development, Key Performance Indicators (KPIs), and Decision Intelligence
Introduction to AI/ML Applications in Data Products
Recommendation Systems and Predictive Modeling
Data Quality Management and Monitoring Protocols
Data Governance, Privacy, and Compliance (Overview of GDPR Principles)
Establishing Trust, Security, and Reliability in Data Products

Day 5

Deployment, Scaling, and Productization
Productizing Data Solutions for End Users
Deployment Strategies and Continuous Integration/Continuous Deployment (CI/CD) for Data Products
Monitoring, Performance Optimization, and Scalability
Data Product Lifecycle Management within Organizations
Monetization Approaches for Data Products
Emerging Trends: Generative AI and Autonomous Data Products
Capstone Project Presentation and Feedback Session

Requirements

  • Participants are expected to have a foundational knowledge of data principles and standard business reporting practices.
  • Prior exposure to Microsoft Excel or comparable elementary data analysis software is advantageous.
  • Understanding the role of data in informing strategic business decisions is highly desirable.
  • The curriculum does not require advanced programming skills or specialized technical expertise.
  • A strong commitment to the fields of data analytics and digital product development for government is mandatory.
 35 Hours

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