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Course Outline

Day 1: Strategic Foundations of Data Products

Introduction to modern data product frameworks and their role in organizational strategy. Examination of the distinctions between traditional data systems and contemporary data products. Analysis of data as a critical strategic asset for public sector operations. Overview of the essential components within a data product ecosystem. Methodologies for identifying high-impact business problems suitable for data-driven solutions. Lifecycle management overview, spanning from initial concept through scaling phases. Review of industry case studies demonstrating successful data product implementations for government applications.

Day 2: Architecture and Design Principles

Core principles guiding the design of effective data products. Identification and analysis of user personas and specific data consumer needs. Comparison of data architecture models, including centralized, Data Mesh, and hybrid approaches. Design methodologies for scalable and resilient data pipelines. Data modeling techniques tailored for analytics and operational requirements. Implementation of APIs and accessibility layers to ensure secure data sharing. Overview of cloud infrastructure capabilities relevant to data products, with reference to major platforms such as AWS, Azure, and GCP.

Day 3: Engineering Frameworks and Implementation

Strategies for data ingestion, contrasting batch processing with streaming methodologies. Comparative analysis of ETL versus ELT frameworks. Best practices for constructing reliable and maintainable data pipelines. Evaluation of data storage solutions, including data lakes, warehouses, and lakehouse architectures. Tools and techniques for data transformation and workflow orchestration. Introduction to real-time data processing technologies. Practical laboratory exercise focused on building a foundational data pipeline.

Day 4: Analytics, Artificial Intelligence, and Governance

Integration of analytical capabilities into data product structures. Development of dashboards, key performance indicators (KPIs), and decision intelligence tools. Overview of AI and machine learning applications within data products, including recommendation systems and predictive modeling frameworks. Strategies for data quality management and continuous monitoring. Principles of data governance, privacy protection, and regulatory compliance, with an overview of GDPR concepts. Measures to ensure trust, security, and reliability in data product operations for government contexts.

Day 5: Deployment, Scaling, and Productization

Strategies for productizing data solutions to meet end-user requirements. Deployment methodologies and continuous integration/continuous deployment (CI/CD) practices specific to data products. Techniques for monitoring performance, optimization, and scaling infrastructure. Management of the data product lifecycle within organizational structures. Approaches to monetization and value realization for data products. Emerging trends in technology, including generative AI and autonomous data products. Capstone project presentation and structured feedback session.

Requirements

The following prerequisites outline the baseline competencies expected of participants in this initiative designed for government professionals: * A fundamental comprehension of data principles and business reporting frameworks is advised. * Proficiency with Microsoft Excel or equivalent introductory data analysis instruments is advantageous. * An understanding of the role data plays in informing organizational decision-making processes is valuable. * Advanced programming expertise or specialized technical backgrounds are not required for enrollment. * A demonstrated interest in data analytics and digital product development is mandatory.
 35 Hours

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