Published annually, the Course Catalog sets out the requirements of the academic programs--the majors, minors, and concentrations. Each Bryn Mawr student must declare her major before the end of her sophomore year. Students may also declare a minor or a concentration, but neither is required for the A.B. degree. Students must comply with the requirements published in the Course Catalog at the time when they declare the major, minor and/or concentration.

The Course Catalog also sets out the College requirements. Students must comply with the College requirements published at the time they enter Bryn Mawr College.

For more information, visit the Catalog Homepage to view the current content. To view Catalogs from previous academic years, visit the Catalog Archives page.

Data are an omnipresent aspect of modern life.  Commercial, governmental, and non-profit organizations increasingly depend on data for their daily operations and planning.  Massive amounts of personal data are generated daily.  How such data are used and interpreted raises significant moral and social issues and is likely to influence the well-being and functioning of individuals, communities, environments and societies.

The Data Science (DS) minor is an interdisciplinary program with courses in a number of departments.  The DS minor provides an opportunity for students to learn about data analytics, computational approaches, data-driven decision making, data structures and management, and the social and ethical implications of data.  

Students can complete the minor by selecting from a broad range of courses.  The Data Science minor is intended to offer pathways for students from all divisions of the college.   Students may complete at Data Science minor as a complement to any major in the TRICO. 

Requirements for the Data Science Minor

The minor comprises six courses. 

One course in each of two foundational areas: 

  • Data Analytic Approaches:  BIOL B250, Computational Methods in the Sciences; CITY B201, Introduction to GIS for Social & Environmental Analysis; CITY B217, Research Methods in Social Sciences; CMSC B206 (Data Structures); ECON B258, Introduction to Econometrics; MATH B205 (Theory of Probability with Applications); or PSYCH B205, Research Methods & Statistics
  • Computing and Data Structures:  DSCI B100, Introduction to Data Science; CMSC B110, Introduction to Computing;  CMSC B113, Computer Science 1;  or BIOL 115, Computing Through Biology

Four additional courses from the list of courses below with the following constraints:

  • At least two of the additional courses must be at the 200 level of above
  • Students can only count 2 courses that they are using for major credit towards the minor

For minor advising please contact, Marc Schulz (, Professor of Psychology and Interim Director of Data Science.

List of Courses

DSCI B100 (Introduction to Data Science)
CMSC B110001 (Introduction to Computing)
CMSC B11000A (Introduction to Computing)
CMSC B11000B (Introduction to Computing)
CMSC B113001 (Computer Science 1)
CMSC B113002 (Computer Science 1)
CMSC B11300A (Computer Science 1)
CMSC B11300B (Computer Science 1)
CMSC B11300C (Computer Science 1)
CMSC B11300D (Computer Science 1)
BIOL 115001 (Computing Through Biology)
BIOL B250 (Computational Methods in the Sciences)
CITY B201 (Introduction to GIS for Social & Environmental Analysis)
CITY B217 (Research Methods in Social Sciences)
ECON B253 (Introduction to Econometrics)
PSYCH B205001 (Research Methods & Statistics)
PSYCH B20500A (Research Methods & Statistics)
PSYCH B20500B (Research Methods & Statistics)
SOCL B265 (Quantitative Methods)
SOCL B265 (Research Methods and Statistical Analysis)
CMSC B206 (Data Structures)
MATH B205 (Theory of Probability with Applications)
CITY B328 (Analysis of Geospatial Data Using GIS)
DSCI/PSYCH B314 (Advanced Data Science: Regression & Multivariate Statistics)
CMSC B380 Recent Advances in Comp Sci-Info Retrieval & Web Search
DSCI B2XX (New Proposed Course) Ethics in Data Science (Norton)
PSYCH 330 Reproducible Research
PSYCH 318 (Data Science with R)
BIOL B330 Ecological Modeling
CMSCH 360A001 Machine Learning
CMSCH 360A00A Machine Learning
CMSCH 360A00B Machine Learning