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.
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)
- 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 or above
- Students can only count 2 courses that they are using for major credit towards the minor
For minor advising please contact, Marc Schulz (email@example.com), Professor of Psychology and Director of Data Science.
List of Courses
|DSCI B100 (Introduction to Data Science)|
|MATH B195 (Statistics for Data Science)|
|CMSC B109 (Introduction to Computing)|
|CMSC B110A(Introduction to Computing)|
|CMSC B113 (Computer Science 1)|
|CMSC B113A (Computer Science 1)|
|CMSC B151 (Data Structures)|
|BIOL B115 (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)|
|ECON B304 (Econometrics)|
|DSCI B201 Ethics in Data Sciences|
|PSYCH B205 (Research Methods & Statistics)|
|PSYCH B205A(Research Methods & Statistics)|
|SOCL B265 (Quantitative Methods)|
|BIOL B330 Ecological Modeling|
|CMSCH 360A001 Machine Learning|
|CMSCH 360A00A Machine Learning|
|CMSC B380 Recent Advances in Comp Sci-Info Retrieval & Web Search|
|CITY B328 (Analysis of Geospatial Data Using GIS)|
|MATH B205 (Theory of Probability with Applications)|
|DSCI/PSYCH B314 (Advanced Data Science: Regression & Multivariate Statistics)|
|PSYCH 318 (Data Science with R)|
|PSYCH 330 Reproducible Research|
|SOCL B327 (Capital & Connections)|
|DSCI B210 Quantifying Happiness: Efforts to study and alter happiness|
|ENVS 307 Introduction to Fisheries Science|
|MUSC H255 Encoding Music|
|HLTH B302 Survey Methods for Health Research|