What is Wellesley's QR & DL requirement and how do students satisfy it?
The Quantitative Reasoning & Data Literacy (QR & DL) degree requirement has two parts, a Quantitative Reasoning component and a Data Literacy component. All students must satisfy both components of the requirement.
The Quantitative Reasoning (QR) component of the QR & DL degree requirement can be fulfilled by successful completion of the introductory quantitative reasoning course, QR 140. Alternatively, students may satisfy the QR requirement by demonstrating their understanding of the material via the QR Assessment by the end of Orientation. Students are required to satisfy the Quantitative Reasoning component of the QR & DL degree requirement in their first year. While we require that students hone these skills early in their time at Wellesley, students who enroll in QR 140 can also enroll in many introductory level courses in quantitative and STEM fields during their first year. No math courses have QR as a prerequisite and several introductory STEM courses that do have QR as a prerequisite also offer sections (labeled with a P) that allow students to take them concurrently with QR 140, including BISC 110P and CHEM 105P.
Learning goals for the Quantitative Reasoning component of the QR & DL requirement, and for QR 140, are: Students will learn to utilize logic, mathematics, and statistics to make decisions as they encounter real world problems in their later coursework, in their future employment, and in their everyday lives as consumers and citizens. By the end of the semester, students will be able to complete the following tasks.
- Set up and solve real-world problems that require multi-step calculations using unit conversions with both familiar and unfamiliar units, scaling, and proportions.
- Calculate with and describe percentages in two-way tables.
- Identify, set up, and solve real-world problems involving linear and exponential growth, using logarithms where appropriate.
- Interpret and perform calculations with numbers in scientific notation.
- Design and carry out multi-step "back-of-the envelope estimations," incorporating geometric formulas for area, volume, and surface area where appropriate.
- Calculate and interpret the mean, median, and standard deviation, and associate these quantities with histograms and written descriptions of data.
- Create spreadsheets to model real-world scenarios and interpret real-world data.
Starting in Fall 2021, QR 140 may be counted toward the MM distribution requirement in addition to satisfying the QR component of the QR & DL requirement. Furthermore, any current students who have already completed QR 140 will receive the MM distribution credit retroactively. Please note that demonstrating proficiency on the QR Assessment during Orientation does not grant MM distribution credit to entering students.
The Data Literacy (DL) component of the QR & DL degree requirement (formerly known as the "QR overlay component") is satisfied by passing a designated Data Literacy course or by receiving AP credit in Statistics (which is equivalent to completion of QR/STAT 150: Introduction to Data Literacy). All Data Literacy courses are designed, at least in part, to teach students how numerical data are analyzed and interpreted in a specific academic discipline. The Committee on Curriculum and Academic Policy has designated individual courses in fields from across the curriculum as ones that satisfy the Data Literacy requirement. Students may complete the Data Literacy requirement at any time during their time at Wellesley. All Data Literacy courses may also be used to satisfy a distribution requirement.
Learning goals for the Data Literacy component of the QR & DL requirement are: Students should learn to identify and construct questions that can be answered with data, to select appropriate methods for collecting and analyzing relevant data to address these questions, and to describe both the conclusions and limitations of such analyses. They should work with their own data and read, interpret, and evaluate other people’s work. By the end of the course, students should be able to complete the following tasks.
- Frame appropriate empirical questions or hypotheses.
- Collect or acquire relevant data, addressing possible biases in the data collection, and read and evaluate the works of other people that are based on data.
- Recognize and explain the role randomness plays in designing studies and drawing conclusions.
- Present data with appropriate graphical displays and numerical summaries, and interpret data presented in such formats, considering what such summaries do and do not reveal.
- Apply appropriate analytical techniques to answer the underlying empirical questions, and interpret and describe the meaning of such analyses when used by others.
Use this link to access the list of Data Literacy courses.