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Course Length
4 half-days
Course Fee
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Acquire the knowledge and frameworks you need to increase your pace of innovation and identify technologies that are poised for powerful disruption. Over the course of four half-days, you’ll explore the forces that are shaping the future of technology—and gain valuable strategies for applying data analysis and theory to technology investment and design decisions, including those involved in planning financial portfolios, research and development portfolios, and public policy.

Course Overview

This course may be taken individually or as part of the Professional Certificate Program in Innovation & Technology.

This course on technological innovation will be organized around three modules on (1) Data, (2) Theory, and (3) Application. In the first module, we will analyze new, large data sets on technological improvement, many of which were collected by the instructor and are the most expansive of their kind. We will cover statistical analysis methods and decomposition models in order to extract useful insight on the determinants of technological innovation. Examples from energy conversion, transportation, chemicals, metals, information technology, and a range of other industries will be discussed.

In the second module, we will cover theories, that have been developed in recent years and stretching back several decades, to explain technological innovation. We will cover the disciplinary origins of these theories, the empirical evidence for or against them, and the usefulness of these theories for practitioners from various fields including engineering, chemicals, private investment, and public policy.

Building on this insight, in the third module we will focus on applying the data analysis methods and theories covered to inform decisions about technology investment and design. The third module will address questions of specific interest to the class. This module will demonstrate the utility of the material covered and how it can be extended to answer a wide range of important questions relating to investment, research and development, manufacturing, and public policy.

Participant Takeaways

  • Developing understanding of how large data sets at various levels of detail can be used to gain insight on the dynamics of technological innovation
  • Learning how to compare the rate of progress of various technologies and products
  • Understanding the state of the art in theories of technological innovation, and their utility for particular questions faced in private industry and the public sector
  • Learning how to apply data analysis and theory to guide investment and design decisions
  • Gaining insight on technological innovation-related decisions faced in designing financial portfolios, research and development portfolios, and public policy

Who Should Attend

This course is designed for people working in industries such as chemicals, life sciences, manufacturing, investment, energy, and public policy makers.

Typical job roles will include:

  • Research and development managers
  • Production/manufacturing operations managers
  • Executive level management in a variety of technology related firms
  • Public policy makers working in technology-related areas
  • Private investors interested in technology-related portfolio optimization


Laptops with a recent version of Excel are required for this course. Participants should have administrator privileges to install programs, as standard Excel packages will be installed and used. Tablets will not be sufficient for the computing activities in this course.

Program Outline

This course meets 9:00am-12:00pm EDT each day. There will be one-hour networking sessions on the first and third days, 12:00-1:00pm.

Day 1 (Module 1: Data)

  • Part 1: Lecture on evidence of technology innovation. What does the data suggest?
  • Part 2: Guided exercise on analyzing technology improvement trends. Participants will assess rates of innovation across various industries.

Day 2 (Module 2: Technology Innovation Theory)

  • Part 1: Lecture on proposed models of technological innovation. How do we explain the observed evidence?
  • Part 2: Guided exercise on comparing the predictive ability of proposed models. We will identify and debate the best-performing models across various industries.

Day 3 (Module 3: Applications)

  • Part 1: Lecture on applying insights from data and theory to decision making in private firms and government. How can we optimize technology design decisions and investment portfolios?
  • Part 2: Lecture on technology portfolio optimization in engineering design, private investment, or public investment.

Day 4 (Module 3: Applications)

  • Part 1: Guided exercise on applying technology portfolio optimization concepts to an industry of interest.
  • Part 2: Summary lecture and discussion on data, theory, and applications.

Links & Resources


News / Articles:


"The overall experience was extremely enriching. I think the program was run and organized very well. The course content and teaching pedagogy was very good and the fact that it was over 5 days actually helped me grasp this kind of topic much better since it needs time and lots of hands on work. Overall I would rate my experience quite high at MIT this summer."
"Good structure, mixture of lectures and exercises, right class size, knowledgeable professors."
"Dr. Trancik and her team did a great job of blending lecture materials, in class projects, night time reading assignments."
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SP - Understanding and Predicting Technological Innovation - Thumbnail


The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 30%|Latest Developments: Recent advances and future trends - 40%|Industry Applications: Linking theory and real-world - 30%
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 70%|Labs: Demonstrations, experiments, simulations - 15%|Discussion or Groupwork: Participatory learning - 15%

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 50%|Specialized: Assumes experience in practice area or field - 30%|Advanced: In-depth explorations at the graduate level - 20%