### A Structured Individual Major

**Directors: Eni Mustafaraj (CS), Casey Pattanayak (MATH/QR), Wendy Wang (MATH), Jeremy Wilmer (PSYC)**

The Data Science major is a structured individual major, consisting of twelve (12) courses that include a concentration area, plus a capstone experience. Students are expected to design their major and concentration in consultation with one of the directors listed above and a second advisor from a department related to the concentration. At least two (2) courses must be at the 300-level, and at least one of these must be from STAT or CS as opposed to the concentration. Students can begin the major requirements in the first or second year and can take MATH 115 and/or MATH 116 in their first year as prerequisites for MATH 205, if needed. Ordinarily, at least statistical modeling, data structures, and two 300-level courses must be taken at Wellesley. The structured individual major in Data Science is large and comprehensive. Students interested in pursuing this major along with another major or minor should consult closely with both the Data Science advisors and the other department.

### Goals of the major:

Data Science lies at the intersection of computer science, mathematics, and statistics. A student pursuing a structured individual major in Data Science will develop a strong foundation in all three areas and complete coursework that emphasizes the integration of the three. By completing a concentration in an applied or theoretical field connected to data analysis, students will learn how data-driven knowledge is produced in that field, gain exposure to its foundations and language, and build the perspective needed to work on field-specific data problems. The capstone will ensure that students experience the challenges of Data Science research. Students will graduate with the critical thinking needed to pose and refine questions that can be answered with data in an ethical way, the statistical skills needed to draw meaning from data appropriately, the computational skills needed to tackle practical data challenges, and the ability to collaborate, communicate, and critique in the context of modern data.

Major requirements:

1. Six (6) foundational courses:

- Introductory Statistics: Any one of STAT 160, STAT 218, BISC 198, ECON 103, POL 299, PSYC 105, or SOC 190
- Statistical Modeling: Either QR/STAT 260 or STAT 318 (Students may take both modeling courses and count the second as an elective.)
- Introduction to Programming: CS 111
- Data Structures: CS 230 (requires CS 111)
- Multivariable Calculus: MATH 205 (requires MATH 116)
- Linear Algebra: MATH 206 (requires MATH 205)

2. Three (3) electives, including at least one from statistics and at least one from computer science, usually chosen from the following list:

- CS 232: Artificial Intelligence
- CS 234: Data, Analytics, and Visualization
- CS 304: Databases with Web Interfaces
- CS 305: Machine Learning
- CS 313: Computational Biology
- CS 315: Data and Text Mining for the Web
- CS 331: Advanced Algorithms
- CS 342: Computer Security and Privacy
- CS 343: Distributed Computing
- STAT 220: Probability
- STAT 221: Statistical Inference
- STAT 228: Multivariate Data Analysis
- QR 260/STAT 260: Applied Data Analysis
- QR 309/STAT 309: Causal Inference
- STAT 318: Regression Analysis and Statistical Models
- ECON 203: Econometrics (for students with concentrations related to economics)

This list of electives is not exhaustive, and other courses in the CS and MATH/STAT curricula or potentially other departments can be appropriate substitutes. We strongly encourage students to talk to the program directors about their interests and learning

goals in order to select the most relevant courses for them.

3. Three (3) electives in an area of concentration, including at least one at the 200- or 300-level. Possible concentrations include but are not limited to digital humanities, social justice, economics, education, global ecology, molecular bioinformatics,

psychology, mathematical/statistical theory, and computer science/data engineering.

4. Students are expected to complete an experiential capstone as part of the Data Science major. The capstone must be approved by the data science directors. Students are required to present their capstone projects at a poster session. Details on the capstone requirement can be found on the Data Science Structured Individual Major website.

### Honors:

A student may achieve honors by writing a thesis, if the student’s GPA in major courses over the 100-level meets the college’s requirements. See Academic Distinctions.

### Further information:

For further information—e.g., possible concentrations and course sequences, and the most up-to-date lists of courses—see the Data Science Structured Individual Major website.