Professor Jessen Havill (Computer Science), Director
Assistant Professors Anthony Bonifonte, Sarah Supp; Academic Administrative Assistant Dee Ghiloni
Committee Laura Boyd (Economics), Michael C. Brady (Political Science), Erin Henshaw (Psychology), Matthew Neal (Mathematics)
Affiliated Faculty Thomas C. Bressoud (Computer Science), Paul A. Djupe (Political Science), Fareeda Griffith (Anthropology and Sociology), Ashwin Lall (Computer Science), Andrew C. McCall (Biology), David White (Mathematics and Computer Science)
Departmental Guidelines and Goals
Global connectivity and innovative technologies generate vast amounts of information that contribute to our understanding and evaluation of nature, human behavior, institutions, society, and beyond. This explosion of evidence to present and address problems is informing major decisions in academe, government, and the private sector. Those with an ability to work with quantitative and qualitative data, big and small, to identify puzzles, consider probing questions, evaluate claims, make inferences, and posit answers will be well positioned to expand knowledge, influence policy, and to be decision makers of the future.
The major in data analytics will provide you with a solid core of mathematics and computer science, followed by four specially designed data analytics courses. Most of these courses are project-based, employing analytic methods, as well as ethics and interdiciplinary research skills, practiced in a variety of application domains. In addition, you will take the skills learned in the classroom and practice them in an internship in a professional setting, and then pursue a capstone project informed by this experience.
Data Analytics Major
The major in Data Analytics (DA) requires a minimum of 44 credits of coursework and a community practicum/internship experience, normally undertaken in the summer before the senior year.
The requirements include:
Introduction to Data Analytics (DA 101)
Discovering Computer Science (CS 111 or 112)
Single Variable or Multivariable Calculus (MATH 123 or 124)
Data Systems (CS 181)
Applied Statistics (MATH 242)
Practicum in Data Analytics (DA 301)
Advanced Methods for Data Analytics (DA 350)
Seminar in Data Analytics (DA 401)
Data analytics summer internship (approved by the Data Analytics Committee)
Three or more electives from one of the participating departments, including at least one quantitative methods course. The purpose of this 3-elective concentration is to give students disciplinary knowledge that they can carry into their internship and senior seminar.
A student may satisfy these electives in one of three ways.
First, a student may concentrate their electives in one of the following disciplines by taking all of the courses for that discipline, as listed below.
Anthropology and Sociology (3 courses)
ANSO 343 or 347
Biology (4 courses)
Economics (4 courses)
Physics (3 courses)
Political Science (3 courses)
Psychology (3 courses)
PSYC 2XX/3XX (except research courses, 370, 410, 361-364, 451-452)
Secondly, a student may take any two of the above analytics-intensive courses (starred), and one additional course in the list above from the same department as one of those analytics-intensive courses.
Finally, a student may submit an individualized elective plan to be considered for approval by the Data Analytics Committee.
Additional Points of Interest
The major in Data Analytics is available to students in the class of 2019 and later.
Students who want to acquire deeper technical skills in data analytics may take additional advanced courses such as Mathematical Modeling (MATH 232), Operations Research (CS/MATH 337), Artificial Intelligence (CS 339), and Statistical Modeling (MATH 401). Students may also pursue a second major in Computer Science or Mathematics. Due to some course overlaps, these options require only 6-7 additional courses.