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Proposing Courses

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Guidance to faculty proposing courses for QRD

Through the Quantitative Reasoning with Data requirement Harvard College students learn quantitative approaches to analyzing data, drawing conclusions, and making predictions to answer questions.  Students should exit a QRD course having gained:

  1. Proficiency in mathematical, statistical, and computational methods. 
  2. Experience in analyzing and drawing conclusions from real data.
  3. Skill in assessing uncertainty.

The QRD committee is eager to review new courses that fulfill these objectives.  Please submit a statement that responds to the following questions to explain how these concepts and skills are employed, examined, and assessed in the course.  Effective statements are typically 1-2 pages long.

In addition, please share the syllabus as well as other course materials (e.g. assignments, lecture sides, and problem sets) that illustrate how the course meets the QRD criteria.

Mathematics, statistics, and computation

  • What mathematical concepts and methods are taught in the course?
  • What statistical concepts and methods are taught in the course?
  • What computational concepts and methods are taught in the course?
  • What is the relative weight of training in mathematics, statistics, and computation in the course?   The QRD committee recognizes that each course will cover these types of methods to different degrees.  QRD requires that each approved course has some content in each of mathematical, statistical, and computational learning, but not necessarily equal content in these areas.
  • What are the expectations for students’ proficiency with the methods and skills taught?  How is that proficiency assessed?

Experience with data

  • How do students engage with data in the course?
  • What is the nature of the data used in the course?

Assessing uncertainty

  • What do students learn about the limits of the methods taught in the course?  How do they demonstrate that understanding?
  • What sources of uncertainty are discussed in the course?  How does the course characterize the interplay of models and data?

Please submit proposal materials to qrd@fas.harvard.edu.  Examples of successful proposals and course materials are available on request.  Members of the QRD committee are also happy to meet with interested faculty before and during the proposal process.