SVP Course Descriptions

The Silicon Valley Program consists of four courses in an off-campus residential setting taught by Claremont McKenna faculty.

Students from the other Claremont Colleges should consult with their respective Registrars’ office and/or department chair about college-specific conditions for participating.


All students will be enrolled in the following required courses: 

ECON 65 CM / ECON 165 CM
Innovation Management / Industrial Organization

This cross-listed course can be taken as a level 1 or a level 2 economics course; the difference between the two lies in the exams. This course uses economics to study firm strategy and industry evolution in contexts where innovation is important. We discuss imperfect competition and the main sources of competitive advantage, describe several stylized facts about innovation, consider factors that impact the generation and diffusion of innovations, study dynamic strategic interaction and how high-tech firms and industries evolve, and develop an understanding of the role and impacts of public policies in innovative industries. ECON 165 serves as an elective for the finance sequence at CMC and counts toward the Science Management major.

  • Prerequisite: ECON 50 CM or equivalent for ECON 65 CM / ECON 101 CM or equivalent for ECON 165 CM
  • Instructor: Filson, D
  • Major Credit: CMC ECON = 1.0 Level 1 elective for ECON 65 CM / CMC ECON = 1.0 Level 2 elective for ECON 165 CM / CMC Science Management = 1.0 core requirement for ECON 165 CM
  • Sequence Credit: 1.0 CMC Financial Economics elective for ECON 165 CM
  • Graduation Credit: 5Cs = 1.0
  • Graded or Credit / No Credit Letter Grade

LEAD 150 CM
Leadership, Innovation and Entrepreneurship in Silicon Valley

This course provides an overview of leadership, innovation, and entrepreneurship theories and constructs, with applications and implications for leading in innovative and cutting-edge organizations in Silicon Valley. Topics will range from the history of Silicon Valley, to leading creative teams, to entrepreneurial start-ups, and the leadership skills necessary to foster innovative organizations. A central theme will be to equip students with the knowledge and skills to be effective leaders in innovative organizations.

  • Prerequisite: None
  • Instructor: taught by Thompson, S with contributions from Riggio, R and Sherman, S
  • Major Credit: None
  • Sequence Credit: 1.0 CMC Leadership capstone course
  • Graduation Credit: 5Cs = 1.0
  • Graded or Credit / No Credit: Letter Grade

INT 030 CM
Silicon Valley Program Internship

This course integrates a full-time internship with career coaching, class discussions, and other professional development activities. This course complements the other coursework in the program to enhance the student’s understanding of the strategies and practices of innovative organizations, firm-level innovation ecosystems, high-tech markets and the regional system of innovation in Silicon Valley and the surrounding area. The professional development activities enhance the students’ understanding of and expertise in a set of career readiness competencies. Internships are obtained in consultation with the program director, and host organizations should either be pursuing innovations themselves or supporting the innovative activities of others (e.g. venture capitalists, consultants, law firms, etc.).

  • Prerequisite: None
  • Instructor: Contributions from the core SVP faculty and staff (Filson D, Thompson, S, Moussa N, LaPierre R)
  • Major Credit: None
  • Sequence Credit: 1.0 CMC Leadership experiential course
  • Graduation Credit: CMC = 1.0; POM = None; PIT = None; SCR = 1.0
  • Graded or Credit / No Credit: Letter Grade

Students will choose one structured independent study course:

Offerings vary by semester depending on student interests and faculty availability (Econ 98 and Econ 198 are offered every semester). The following list includes preapproved courses that have been provided in recent semesters. Most students select a preapproved course, but CMC and Pomona students may also be able to arrange a unique independent study in an area of their choice through an appropriate arrangement with a Claremont-based professor. For example, recent students have pursued tailored independent studies in Economics, Math and Psychology.

ECON 98 CM / 198 CM
Organizing for Innovation / Economics of Innovation

This cross-listed course can be taken as a level 1 or a level 2 economics course. This course guides students in the Silicon Valley Program through several individual and group projects grounded in economics that provide insights into how firms and other entities organize for innovation. The material considers how firm decisions, organizational structures, public policies and the surrounding environment impact the nature and amount of the innovations an organization can generate along with its ability to appropriate returns. Links between the course content and the internship experiences are developed: the internship settings are like “labs” that provides examples of the concepts and frameworks developed in the course. For this purpose, students must interpret their internship experiences broadly and learn about their organizations and the corresponding business environments, not just the internship tasks and responsibilities.

  • Prerequisite: ECON 50 CM or equivalent for ECON 98 CM / ECON 101 CM or equivalent for ECON 198 CM
  • Instructor: Filson, D
  • Major Credit: CMC ECON = 1.0 Level 1 elective for ECON 98 CM / CMC ECON 1.0 Level 2 elective for ECON 198 CM
  • Sequence Credit: 1.0 CMC Leadership breadth course
  • Graduation Credit: 5Cs = 1.0
  • Graded or Credit / No Credit: Letter Grade

ECON 120 CM
Statistics

This course is a level 1 economics course. It provides an introduction to probability theory and the logic of statistical inference with applications to economics and business. Topics include measures of central tendency and dispersion, point and interval estimation, hypothesis testing, correlation, decision theory, and regression analysis.

  • Prerequisite: ECON 50 CM or equivalent and MATH 030 CM or equivalent
  • Instructor: Keil, M
  • Major Credit: CMC ECON = 1.0 Level 1 elective
  • Sequence Credit: None
  • Graduation Credit: 5Cs = 1.0
  • Graded or Credit / No Credit: Letter Grade
DS 181 CM
Advanced Projects in Data Science: Silicon Valley Program

Students pursuing data science can petition to replace DS 180 Advanced Projects with an appropriately constructed SVP independent study (we refer to this course as DS 181, but the prefix and name are subject to change). Interested students must identify a professor willing to supervise the course and obtain approval from Professors Mark Huber and Jeho Park. Approval will normally involve satisfying several conditions that ensure that key features of DS 181 are comparable to those of DS 180 (students can refer to the CMC course catalog for a description of DS 180):

a. The student’s internship involves DS in key ways: programming, analytics, etc. The hours expected to be spent on DS projects associated with the internship should meet or exceed the hours students normally spend on projects in DS 180.   

b. The student’s DS tasks associated with the internship involve working as part of a team, not in isolation. The team leader(s) should have a DS background and/or a field-specific background appropriate for supervising the student’s work, facilitating interactions within the team, and interpreting the student’s results. The goal is to have a team structure comparable to that in DS 180.  

c. The independent study will leverage the internship to the extent possible, but it will have distinct requirements that differ from those of the internship per se. In particular, the course requirements will include deliverables that would not normally be generated through the student’s employment such as one or more papers, presentations or other items. For example, a student might be required to write a paper exploring and explaining the tools and approaches used for particular types of analyses.  

DS program administrators are under no obligation to approve any particular petition: each case will involve considering the employer, the student and the project to determine whether the goals of DS 180 are being met.  

  • Prerequisites: Statistics, CSCI 036 CM or the equivalent, and at least 2 other Data Science sequence courses
  • Instructor: TBD
  • Sequence Credit: CMC DS = 1.0 (replaces DS 180)
  • Graduation Credit: 5Cs = 1.0
  • Graded or Credit / No Credit: Letter Grade