The BS Data Science has a "hidden" requirement: Calculus is required for MATH 280 but does not count toward the 10 courses of the major.
Five (5) courses in Data Science, one (1) course in Computer Science, one (1) course in Mathematics, and
three electives. Electives may be courses with either a DATA or CS prefix, pre-approved courses from
other departments, or courses approved in consultation with your major advisor.
If you wish to use more than one CS-prefixed elective, consult with your adviser.
Requirements
Dependency Graph
CS__ 151 : Intro to Programming in Python
DATA 151 : Intro to Data Science in R
Statistics Requirement, choose one of
DATA 152 : Inferential Statistics
MATH 138 : Introduction to Applied Statistics
Machine Learning Requirement, choose one of
DATA 252 : Models and Machine Learning
CS__ 475 : Machine Learning*
MATH 280 : Math for Data Science*
DATA 351 : Data Management with SQL
DATA 352W: Ethics, Teamwork, Communications
Elective
Elective
Elective
BS/MS 3+1
Two (2) courses in Data Science, one (1) course in Computer Science
and
two electives. Electives may be courses with either a DATA or CS prefix, pre-approved courses from
other departments, or courses approved in consultation with your major advisor.
Calculus is required but does not count toward as degree progress.
If you wish to use more than one CS-prefixed elective, consult with your adviser.
The M.S. in Data Science is a PROFESSIONAL and TERMINAL degree. It is not a research degree, does not have a thesis component, and is not intended to prepare students for doctoral (Ph.D.) study. Students interested in graduate school with a research focus should consult their advisor before committing to the program. As a professional degree, the M.S. is tuition-funded, rather than grant-funded, though undergraduate financial aid agreements apply.
Students must ensure they meet the undergraduate credit requirement to graduate on time. In practice, this means taking four full courses (16 credit hours) every semester during the three undergraduate years, and an additional course over the three years, usually by taking two half-courses (2 credit hours each) to avoid additional tuition costs. I recommend MATH 102X, MATH 153, and ARTH 10X classes.
There are a few courses that Dual Degree students should not take because they are redundant with graduate level coursework:
DATA 252 : Models and Machine Learning
DATA 351 : Data Management with SQL
DATA 352W: Ethics, Teamwork, Communications
Undergraduate Core
CS__ 151 : Intro to Programming in Python
DATA 151 : Intro to Data Science in R
Statistics Requirement, choose one of
DATA 152 : Inferential Statistics
MATH 138 : Introduction to Applied Statistics
MATH 280 : Math for Data Science*
Elective
Elective
Master's Core
DATA 502 : Data Visualization and Presentation
DATA 503 : Fundamentals of Data Engineering
DATA 504W: Data Ethics, Policy, and Human Beings
DATA 505 : Applied Machine Learning
DATA 510 : Graduate Capstone
500+ Elective 1/4
500+ Elective 2/4
500+ Elective 3/4
500+ Elective 4/4
MINOR
Two (2) courses in Data Science, one (1) course in Computer Science, one (1) course in
Mathematics, and
two electives. Electives may be courses with either a DATA or CS prefix, pre-approved courses from
other departments, or courses approved in consultation with your major advisor.
If you wish to use more than one CS-prefixed elective, consult with your adviser.