Innovation Lab: Design Thinking in Data Science
Solve complex business problems through human-centered design and machine learning.
Applies human-centered problem-solving to business challenges with machine learning.
The Innovation Lab brings together design thinking principles and data science techniques to solve complex, real-world problems. Learners focus on understanding user needs, reframing data questions, and iteratively building solutions that are both technically sound and human-centered.
Key Features
- Combines creative ideation with analytical rigor
- Emphasizes empathy, usability, and stakeholder context
- Encourages iterative solution design using real-world datasets
- Focuses on data framing, model interpretability, and decision impact
- Supports collaborative, project-based learning
Ideal For
- Learners who want to blend creativity with analytics
- Professionals in product design, UX, strategy, or research
- Teams looking to build data solutions that are both useful and usable
- Anyone interested in approaching data science from a user-centered perspective
Certificates
• Design Thinking for Business Intelligence
Learn to apply design thinking principles to BI projects. Focus on understanding stakeholder needs, framing data questions, and designing dashboards and reports that drive decision-making and business value.
• Design Thinking for Data Analytics
Use a human-centered approach to uncover insights from data. Emphasis is placed on problem framing, iterative analysis, and storytelling to ensure analytics solutions are both relevant and actionable.
• Design Thinking for Machine Learning
Integrate user empathy and system thinking into the ML development process. Learn how to frame ML problems, evaluate model usability, and design responsible solutions that address real-world challenges.
• Design Thinking for Generative and Agentic AI
Explore how to co-create generative and agentic AI systems with users in mind. Focus on ethical prototyping, prompt design, autonomous workflows, and stakeholder engagement to ensure AI solutions are impactful and responsible.
