The Data Science Interdisciplinary Minor at Davidson

The data science interdisciplinary minor is open to students from all academic divisions who wish to develop skills in using and analyzing data.

Such data skills can complement and enhance liberal arts study across a broad range of subject matters and interests. In the humanities, the minor provides technical skills that can powerfully complement the writing, creativity and critical analysis encouraged in humanities courses. In social sciences, the minor promotes facility with data supplements and enhances your ability to analyze and understand societal questions and problems. In natural sciences and mathematics, the minor deepens data management skills and can improve quantitative analysis in all major fields.

Whatever your major, a data science minor provides transferable skills such as understanding data and its different forms, communicating effectively about data, methods, and conclusions and drawing meaning from data.

Application Process

If you wish to declare a data science minor you must first complete the application. You should do this in discussion with one of our coordinators, Prof. Laurie Heyer (Mathematics and Computer Science) at laheyer@davidson.edu or Prof. Pat Sellers (Political Science) at pasellers@davidson.edu as early as possible, and no later than the fall semester of senior year.

Courses You Might Take

SOC 392

Quantitative Data Analysis

Instructor
Deckard, Kaufman

The purpose of this class is to prepare you as a future producer and evaluator of high-quality quantitative research - whether as a social scientist, as a decision-maker in a corporate setting, or as a designer and…

BIO 309

Genomics

Instructor
M. Campbell, D. Thurtle-Schmidt

Students use published resources to understand how genome-scale information (e.g., DNA sequences, genome variations, transcriptomes, proteomes, and clinical studies) can provide a systems biology perspective. Students also use databases and bioinformatics tools to analyze data and…

CSC 221

Data Structures

Instructor
Cameron

A study of abstract data types, including lists, stacks, queues, and search tables, and their supporting data structures, including arrays, linked lists, binary search trees, and hash tables. Implications of the choice of data structure on the efficiency of…