DATA SCIENCE

BACHELORS OF SCIENCE

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
  1. CS__ 151 : Intro to Programming in Python
  2. DATA 151 : Intro to Data Science in R
  3. Statistics Requirement, choose one of
    • DATA 152 : Inferential Statistics
    • MATH 138 : Introduction to Applied Statistics
  4. Machine Learning Requirement, choose one of
    • DATA 252 : Models and Machine Learning
    • CS__ 475 : Machine Learning*
  5. MATH 280 : Math for Data Science*
  6. DATA 351 : Data Management with SQL
  7. DATA 352W: Ethics, Teamwork, Communications
  8. Elective
  9. Elective
  10. Elective
CS 151:
Intro to
Programming in Python
CS 151:...
DATA 151:
Intro to
Data Science in R
DATA 151:...
DATA 152:
Inferential
Statistics with R
DATA 152:...
MATH 280:
Math for Data Science
MATH 280:...
DATA 252:
Models and
Machine Learning
DATA 252:...
DATA 351:
Data Management
 in SQL
DATA 351:...
DATA 352W:
Ethics, Teamwork,
Communication
DATA 352W:...
MATH 138: Introduction to Applied
 Statistics
MATH 138: Introducti...
OR
OR
Any Calulus or Instructor Consent
Any Calulus or Instr...
CS 475:
Machine
Learning*
CS 475:...
OR
OR
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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

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.

  1. CS__ 151 : Intro to Programming in Python
  2. DATA 151 : Intro to Data Science in R
  3. Statistics Requirement, choose one of
    • DATA 152 : Inferential Statistics
    • MATH 138 : Introduction to Applied Statistics
  4. Elective
  5. Elective