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Course Outline

Day 1 

 Foundations of Data Products & Strategy
Overview of Contemporary Data Products
Differentiating Data Products from Traditional Data Systems
Positioning Data as a Strategic Business Asset
Core Components of a Data Product Ecosystem
Identifying Viable Business Problems for Data Solutions
Overview of the Data Product Lifecycle (Ideation through Scaling)
Industry Case Studies: Exemplary Successful Data Products

Day 2 

 Data Product Design & Architecture
Core Principles of Data Product Design
Analyzing User Personas and Data Consumers
Data Architecture Models (Centralized vs. Data Mesh vs. Hybrid)
Architecting Scalable Data Pipelines
Data Modeling for Analytics and Operational Applications
APIs and Data Accessibility Layers
Cloud Infrastructure for Data Products (Overview of AWS / Azure / GCP)

Day 3 

Data Engineering & Implementation
Data Ingestion Methodologies (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
Incorporating 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 for End Users
Deployment Strategies and CI/CD for Data Products
Monitoring, Performance Optimization & Scaling Managing the Data Product Lifecycle within Organizations
Monetization Approaches 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 recommended.
  • Proficiency with Excel or comparable basic data analysis utilities is advantageous.
  • Familiarity with the role of data in business decision-making is beneficial.
  • No advanced programming expertise or technical background is necessary.
  • A strong interest in data, analytics, and digital product development is essential.
 35 Hours

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