Quantitative Reasoning and Data Literacy

Quantitative Reasoning and Data Literacy in the Core Curriculum

Modern life and social interactions in the 21st century are heavily influenced by data. It permeates every aspect: from our personal lives to our roles as citizens in a democratic society. But data – in and of itself – is meaningless. What transforms data into a powerful tool is the ability to know how to gather, assess and extract meaning from it, and to understand both the utility and the limitations of data. To this end, the importance of data interpretation in contemporary society is difficult to overstate. We believe that a well-rounded liberal science and arts education must provide students with the power to use data in order to communicate cogent arguments about complex phenomena.

The Quantitative Reasoning and Data Literacy (QRDL) requirement in the Core Curriculum will help students learn to understand how real-world interpretation of data progresses from a set of numbers, frequently incomplete and with measurement errors, to a visual distillation that clearly conveys meaning. While mathematics and statistics provide a necessary foundation for quantitative reasoning and data literacy, the power of pure mathematics lies in its abstraction. In contrast, QRDL emphasizes a distinctive variety of concrete contexts and applications. Specifically, the QRDL requirement will differ from a mathematics requirement in two important ways. Emphasis will be first on the applications of quantitative methods to academic real-world subjects and second on understanding the inherent messiness of data. This “messiness” might require the use of estimations and approximations, often known as back-of-the-envelope calculations, to determine an appropriate launch point for more precise analysis.

As instructors design their courses, they need to consider three categories of learning goals, and the specific goals within each category, that collectively serve to anchor the QRDL curriculum:

Build a Quantitative Data Toolbox

  • To gather and organize data to highlight patterns and make it accessible for analysis.
  • To recognize the roles of estimation, approximation and precision as tools important in quantitative analyses.
  • To become comfortable performing and/or evaluating quantitative analyses.
    • Select and apply pertinent statistical methods to explore and analyze data on one’s own.
      • and/or
    • To rigorously evaluate quantitative data and interpretations of these data when presented by others.

Translate and Make Meaning

  • To translate real-world and disciplinary questions or intellectual inquiries into quantitative frameworks using graphical, symbolic, and numerical methods.
  • To be aware of the strengths and weaknesses, uncertainties and biases inherent in quantitative data and quantitative analyses and to incorporate this understanding into the interpretation and representation of data.
  • To think critically about numerical representations and to employ them in one’s critical thinking and when making complex claims.

Communicate and Consider Impact

  • To effectively communicate statistical methods and results both visually and textually to a diversity of audiences with considerations to contextual, social, or ethical issues.

Faculty who want their course to count toward the Quantitative Reasoning and Data Literacy requirement must submit a short application form by February 1, 2024.

Quantitative Reasoning and Data Literacy Course Application

This form will ask you for the following:

  • Basic course info. These questions include semester(s) the course is offered, credits, caps, prerequisites, etc.
  • A syllabus from a recent offering of the course. This should include a course description, learning goals, and information about topics covered in the course.
  • QRDL learning goals. Faculty will be asked to explain how their course meets the seven goals distributed among the three categories of learning goals. This should take just 1-2 sentences for each goal.
  • 2-3 representative assignments from a recent offering of the course. These can be readings, tests, papers, problem sets. You will have the opportunity to describe in 1-2 sentences how each assignment helps students practice the QRDL learning goals. 
  • An invitation to ask any questions. We recognize that the learning goals might require some (hopefully minor) changes to an existing course, and you might have questions about how to meet some of the learning goals. We welcome the opportunity to talk and be helpful and collaborative in this process.

Importantly, the form also gives you the option to complete the application solely online or to initiate a conversation with the QRDL Committee if you prefer. Our goal is to ensure that students are able to take quality courses that richly address the QRDL learning goals while minimizing the administrative burden on faculty.

The QRDL Committee will notify instructors of the approval status of their course prior to the start of student registration.

Approved QRDL courses for the 2024-2025 Academic Year will be listed here following the QRDL Committee’s review period in February. Sample syllabi and assignments will be included for a few courses to provide helpful models for future course design and application.

Quantitative Reasoning and Data Literacy in the Core Curriculum Committee

Oded Meyer, Chair, Department of Mathematics and Statistics, College of Arts & Sciences

Kirstin Anderson, McDonough School of Business

Rhonda Dzakpasu, Department of Physics, College of Arts & Sciences

Heidi Elmendorf, Department of Biology, College of Arts & Sciences

Rodrigo Maillard, Department of Chemistry, College of Arts & Sciences

James Mattingly, Department of Philosophy, College of Arts & Sciences

Erblin Mehmetaj, Department of Mathematics and Statistics, College of Arts & Sciences

Irfan Nooruddin, School of Foreign Service

Rebecca Ryan, Department of Psychology, College of Arts & Sciences

Barbara Schone, McCourt School of Public Policy

Grace Yang, Department of Computer Science, College of Arts & Sciences

Wu Zeng, Department of Global Health, School of Health