The Quantitative Reasoning and Data Literacy Requirement

The Quantitative Reasoning and Data Literacy degree requirement has two parts, a Quantitative Reasoning (QR) component and a Data Literacy (DL) component. All students must satisfy both components of the requirement.

Quantitative Reasoning (QR)

The QR component of the Quantitative Reasoning and Data Literacy degree requirement is satisfied either by satisfying the QR Assessment by the end of Orientation or by passing QR 140, the introductory QR course that builds quantitative skills in the context of real-world applications. Students are required to satisfy the QR component of the Quantitative Reasoning and Data Literacy degree requirement in their first year so that they may enroll in the many courses for which quantitative skills are a prerequisite. QR 140 can be used to fulfill the Mathematical Modeling (MM) distribution requirement.

Learning goals for the QR component of the Quantitative Reasoning and Data Literacy requirement, and for the introductory course, 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.

Data Literacy (DL)

The DL component of the Quantitative Reasoning and Data Literacy degree requirement is satisfied by passing a designated DL course or by receiving AP credit in Statistics (which is equivalent to completion of QR/STAT 150: Introduction to Data Literacy). All DL 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 DL component of the Quantitative Reasoning and Data Literacy degree requirement. Students may complete the DL component of the requirement at any time during their time at Wellesley. All DL courses may also be used to satisfy a distribution requirement.

Learning goals for the DL component of the Quantitative Reasoning and Data Literacy degree 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.

Data Literacy Course Options

Wellesley College offers a range of courses that can be used to satisfy the DL component of the Quantitative Reasoning and Data Literacy degree requirement. These courses include introductory statistics courses offered within a variety of disciplines, including Biological Sciences, Economics, Sociology, Political Science, Psychology, and Mathematics and Statistics. Other Data Literacy courses include significant emphasis on data and statistics but do not focus solely on statistical analysis and are offered across the curriculum in departments including Art History, Astronomy, Environmental Studies, and Geosciences. The complete list of currently offered courses that satisfy the DL component of the Quantitative Reasoning and Data Literacy degree requirement can be found below. Please see the full course descriptions under each department or program for details on prerequisites and the applications emphasized in each course. Note that: (1) All DL courses offered at Wellesley require satisfaction of the QR component of the Quantitative Reasoning and Data Literacy degree requirement as a prerequisite. (2) Any individual course on the list below can be used to fulfill both the DL component of the Quantitative Reasoning and Data Literacy requirement and a distribution requirement.

Introductory statistics courses that can be used as prerequisites for further study in statistics are indicated with a * in the list below. Because AP credit in Statistics is equivalent to completion of QR/STAT 150: Introduction to Data Literacy, which cannot be used as a prerequisite for higher-level courses in statistics, students with such AP credit who wish to continue their study of statistics must enroll in one of the starred introductory statistics courses on this list. Interested students should consult individual departments or programs for details on the various introductory statistics course options and for suggestions about choosing an appropriate first course.

ARTH 222 / MAS 222	Network Analysis for Art History	1.0
ASTR 200	Exoplanetary Systems	1.0
BISC 109	Human Biology with Laboratory	1.25
BISC 111	Introductory Organismal Biology with Laboratory	1.25
BISC 111T	Introductory Organismal Biology with Laboratory (Tropical Island)	1.25
BISC 113 / BISC 113Y	Exploration of Organismal Biology with Laboratory	1.0
* BISC 198	Statistics in the Biosciences	1.0
BISC 201	Ecology with Laboratory	1.25
CHEM 103	Elements and the Environment	1.0
CHEM 120	Intensive Introductory Chemistry with Laboratory	1.25
CHEM 205	Chemical Analysis and Equilibrium with Laboratory	1.25
CHEM 330	Physical Chemistry I with Laboratory	1.25
CHEM 361	Analytical Chemistry with Laboratory	1.25
CS 234	Data, Analytics and Visualization	1.0
* ECON 103 / SOC 190	Introduction to Probability and Statistical Methods	1.0
ECON 203	Econometrics	1.0
ES 100	Introduction to Environmental Science and Systems	1.0
ES 101 / ES 101Y	Fundamentals of Environmental Science with Laboratory	1.0
GEOS 101	Earth Processes and the Environment with Laboratory	1.25
PHYS 202	Introduction to Quantum Mechanics and Thermodynamics with Laboratory	1.0
PHYS 210	Experimental Techniques	1.0
PHYS 310	Experimental Physics	1.0
* POL 299	Introduction to Research Methods in Political Science	1.25
* PSYC 105	Introduction to Data Analysis in Psychological Science	1.0
QR 150 / STAT 150	Introduction to Data Literacy: Everyday Applications	1.0
QR 190	Epidemiology	1.0
QR 309 / STAT 309	Causal Inference	1.0
* STAT 160	Fundamentals of Statistics	1.0
* STAT 218	Introductory Statistics and Data Analysis	1.0