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Data and AI Driven Product and Service Design

Curriculum

  • 1 Section
  • 8 Lessons
  • 8 Weeks
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  • Lessons
    8
    • 1.1
      Digital Transformation Through Lean and AI-Driven Product Development
    • 1.2
      Customer Development and Design Thinking
    • 1.3
      UX in Agile Development Using Scrum
    • 1.4
      From Discovering Early Adopters to Deploying Big Data Analytics to Scale
    • 1.5
      Go-to Market Strategy, Growth Hacking, and Tech Stack
    • 1.6
      Design to Development Hand-off: Information Architecture, Wire-frames, Design System, and Prototype
    • 1.7
      From Workflow to Agents: API and AI As Product
    • 1.8
      Qualitative and Quantitative Research and Testing

From Discovering Early Adopters to Deploying Big Data Analytics to Scale

https://hkdesign.org/wp-content/uploads/2025/08/The_Product_Compass__Navigating_from_Intuition_to_Insight.mp4

Adopting a Lean approach begins with developing your minimum viable product (MVP) and, crucially, finding its early adopters—the minimum viable segment (MVS) who share pressing needs. This focused segment serves as the launchpad for scalable growth. In today’s landscape, where big data technologies and analytics are ubiquitous, product-led growth empowers organizations to capture, transform, and distribute both structured and unstructured data—from machine-generated and human-supplied sources—across multiple channels (omni-channel). The result is a robust platform that enables full automation of supply chains and customer journey touchpoints.

As data science and design thinking increasingly become integral to agile product development, business, product, and customer analytics are now standard practice. Product professionals trained in Lean methodologies rely heavily on data metrics to validate business models and drive growth. The Lean Analytics framework, with its staged approach, emphasizes rapid product adoption, viral customer acquisition, and continual retention and revenue expansion. With large language models (LLMs) reaching greater maturity, artificial intelligence is not only embedded throughout the product lifecycle for analytics purpose but is also becoming a defining feature of modern products.

Self-study Videos

  1. How Airbnb’s Early Adopters Saved the Company (9 min 7 sec)
  2. Who are your Early Adopters? (7 min 14 sec)
  3. Find your Minimum Viable Segment, and create a Critical Product to Succeed (3 min 52 sec)
  4. What is lean analytics? (12 min 31 sec)
  5. Introduction to the CRISP-DM Methodology (Analytics & Data Science) (5 min 19 sec)
  6. Data Sources of Big data (3:21 min)
  7. Retail Digital Supply Chains: Facing an omnichannel customer-driven landscape (4 min 33 sec)
  8. Data Drivers: Customer Journey Mapping with Big Data (11 min 36 sec)
  9. Data Analytics for Better Product Decision Making (20 min 38 sec)
  10. How to Be an AI Product Manager (17 min 52 sec)
  11. What is Platform Product Management (28 min 34 sec)
  12. 10 Metrics Every SaaS PM Should Use (28 min 4 sec)
UX in Agile Development Using Scrum
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