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Michael Hopkins teaches MATH 121, Linear Algebra and Applications.

Teaching a Course

Information for faculty and departments on how to align courses with the QRD requirement.

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Guidelines for a Successful QRD Course

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. 
  1. Experience in analyzing and drawing conclusions from real data.
  1. Skill in assessing uncertainty.

Courses should engage all three methodological domains (mathematics, statistics, and computation), but the level of engagement among those three domains will vary. Some courses might engage all three domains equally. Others might engage one domain in depth and two moderately. And some might engage two in depth, while gesturing towards the third by recognizing opportunities to explore it beyond the scope of the course. (An example of the third: a course focused equally on mathematics and computation may not require any direct engagement with statistics but should introduce students to aspects of the course that statistics addresses with greater depth and, perhaps, how and where students might study such areas beyond the course.)

Students should learn the shortcomings of any data gathered about the world (e.g., historical and representation biases) and the limitations of the methods applied to these data (e.g., the role of approximating assumptions, overfitting, causal ambiguity).  

While different instructors may adapt QRD courses to be their own, please note that courses with a QRD designation are expected to consistently meet all of the aforementioned criteria in every offering. 

Course Review and Reapproval Process

The QRD committee is responsible for reviewing and reapproving courses at regular intervals to ensure that they continue to fit with the requirement. The committee may look at multiple materials in its review, including syllabi, Q scores, assessments, and instructor comments, in order to consider the following questions:

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?

The committee may elect to review a course more frequently if the prior review or offering raised significant concerns; if the course contains high numbers of students fulfilling their QRD requirement; if the course earns low Q scores; or if the course has a change in instructors.   

The committee views the review process as a way to improve QRD courses and endeavors to give as much constructive feedback as it can to faculty and departments. 

Proposing a QRD course 

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. 

Please submit proposal materials to qrd@fas.harvard.edu. Members of the QRD committee are also happy to meet with interested faculty before and during the proposal process.

2024-2025 QRD Committee