Data Science and Innovation for Impact


• Understand the fundamental principles and main concepts of managing innovation, as well as the different ways of
developing and managing an innovative culture within the organization;
• Understand the different contexts and sources of innovation.
• Understand the intrapreneurial process and apply intrapreneurial best practices to innovation projects or ventures inside
existing organizations
• Recognize how the economics and governance of different organizational types (firms, public health care organizations)
affect their behavior in innovation.
• Develop the competences to integrate a number of factors, internal and external to the organization, in decision-related
• Upon successful completion of the course each student should have demonstrated their ability to articulate innovation
management issues and to apply the knowledge acquired in a real context.

General characterization





Responsible teacher

Pedro Oliveira


Weekly - Available soon

Total - Available soon

Teaching language





• Tidd, J. (2006). “A review of innovation models” (discussion paper, Imperial College London)
• Perkmann, M., Salter, A. (2012) "How to create productive partnerships with universities", MIT Sloan Management Review
• Gans, J., and S. Stern (2016). “The Intellectual Property Strategy”, Entrepreneurial Strategy, Chapter 9.
• Oliveira, P., L. Zejnilovic, S. Azevedo, A.M. Rodrigues and H. Canhão (2019) "Peer-adoption and development of health
innovations by patients: a national representative study of 6204 citizens,” Journal of Medical Internet Research, 21 (3):
• Logg, J. M., “Algorithm appreciation: People prefer algorithmic to human judgment”, HBR, 2018
• Data Science Project Scoping, Blog Post, Data Science for Social Good Foundation, 2017.
• Lee, M. K., “Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic
management”, Big Data & Society, 2018.
• Miller, A., “Want Less-Biased Decisions? Use Algorithms.”, HBR, 2018.

Teaching method

• Lectures will be the predominant form of presentation and will be complemented by a variety of teaching approaches
including discussion of case studies and in-class exercises. A full interchange between the instructor and the participant is
expected. Preparation before class is of crucial importance.
• The course will also benefit from guest lecturers who will visit the class to share their experience in managing innovation.

Evaluation method


Subject matter

Intro to Innovation Management
• Uncertainty and risk
• From linear technology-push to dynamic models of innovation
• The role of basic research in innovation
• Firm internal vs. external sources of innovation
Intellectual Property Strategy
Open and User Innovation
• Closed vs. open innovation
• User innovation and necessity as a driver of innovation
• User innovation in healthcare
• Patient Innovation
Intro to Data Science for Social Impact
• Data science in practice – the landscape
• Data-driven decision making
• Technology and Skills for Data Science
• Infrastructure for data science
• Machine Learning
• Data-science as an innovation tool
Building Innovative Data-driven solutions for the society
• Identifying needs and finding innovative solution for societal problems
• Action-driven Data Science Project Scoping
• Data Science Pipeline
• Implementation of Data Science Projects
Social impact, ethics, and responsible data science


Programs where the course is taught: