Program Requirements and Opportunities

Published annually, the Course Catalog sets out the requirements of the academic programs--the majors, minors, and concentrations. Each Bryn Mawr student must declare a major before the end of the 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 B151 (Data Structures); ECON B258 Introduction to Econometrics; MATH B195 (Statistics for Data Science); MATH B205 (Theory of Probability with Applications); PSYCH B205 Research Methods & Statistics or SOCL B265 (Quantitative Methods)
  • Fundamentals of Computing:  DSCI B100, Introduction to Data Science; CMSC B110. Introduction to Computing; CMSC B113, Computer Science I;  or BIOL 115, Computing Through Biology

Four additional courses from the list of courses below.

Additional Minor Guidelines/Requirements:

  • Students can only count a total of 2 courses that they are using for major credit (or another minor) towards the minor
  • At least two of the additional four courses beyond the two foundational requirements must be at the 200 level or above

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

List of Courses

BIOL B115 (Computing Through Biology)
BIOL B250 (Computational Methods in the Sciences)
BIOL B330 Ecological Modeling
CITY B201 (Introduction to GIS for Social & Environmental Analysis)
CITY B217 (Research Methods in Social Sciences)
CITY B328 (Analysis of Geospatial Data Using GIS)
CMSC B109 (Introduction to Computing)
CMSC B110A(Introduction to Computing)
CMSC B113 (Computer Science 1)
CMSC B113A (Computer Science 1)
CMSC B151 (Data Structures)
CMSC B380 Recent Advances in Comp Sci-Info Retrieval & Web Search
CMSC B383 (Recent Advances in Computer Science: Computational Text Analysis)
CSMC H265 (Critical Study of Data and Algorithms)
CMSCH 360A001 Machine Learning
CMSCH 360A00A Machine Learning
DSCI B100 (Introduction to Data Science)
DSCI B201 Ethics in Data Sciences
DSCI B210 Quantifying Happiness: Efforts to study and alter happiness
DSCI/PSYCH B314 (Advanced Data Science: Regression & Multivariate Statistics)
DSCI B315 Bayesian and Frequentist Statistical Inference
ECON B253 (Introduction to Econometrics)
ECON B304 (Econometrics)
ENVS 307 Introduction to Fisheries Science
GEOL B104 The Science of climate change
HLTH B302 Survey Methods for Health Research
MATH B104  Basic Probability and Stats
MATH B195 (Statistics for Data Science)
MATH B205 (Theory of Probability with Applications)
Math 208 (Introduction to Modeling and Simulation)
MATH H201 (Linear optimization)
Math B295 (Select Topics in Mathematics): “Math Modeling and Sustainability” and “Statistics with R” versions of this course
MUSC H255 Encoding Music
PHIL B258 Data Ethics in Social Media
POLS B233 (Intro to Research Design and Data Analysis)
PSYCH 318 (Data Science with R)
PSYCH 330 Reproducible Research
PSYCH B205 (Research Methods & Statistics)
PSYCH B205A(Research Methods & Statistics)
SOCL B265 (Quantitative Methods)
SOCL B327 (Capital & Connections)

Contact Us

Data Science

Marc Schulz
Professor of Psychology on the Sue Kardas Ph.D. 1971 Professorship and Director of Data Science

Nina Fichera
Administrative Support Staff