Other Programs

This page will show degree paths other than the two B.S. degrees offered by Computing and Data Programs.

Double-major programs

B.S. Computer Science and Data Science, (12 Courses) [Show] "B.S. Computer Science and Data Science, (12 Courses) [Hide]

Distribution

  • 5 courses in Computer Science
  • 5 courses in Data Science, 4 if taking Statistics outside Data Science
  • 2 courses in Mathematics, 3 if taking Statistics or Calculus in Mathematics, 4 if taking both

Requirements

  1. Statistics Requirement, choose one of
      

    • DATA 152 : Inferential Statistics

    • MATH 138 : Introduction to Applied Statistics
  2. DATA 151 : Intro to Data Science in R

  3. CS   151 : Intro to Programming in Python

  4. CS   152 : Data Structures

  5. MATH 251W: Foundations of Advanced Mathematics

  6. MATH 280 : Math for Data Science*
  7. Machine Learning Requirement, choose one of
      

    • DATA 252 : Models and Machine Learning

    • CS__ 475 : Machine Learning*
  8. CS   261 : Software Development

  9. CS   271 : Networks and Systems

  10. CS   351 : Analysis of Algorithms

  11. DATA 351 : Data Management with SQL

  12. DATA 352W: Ethics, Teamwork, Communications
*Denotes classes with requirements that do not count toward the minor.

Dependency Graph

CS 152, MATH 251W, DATA 351, DATA 352W require CS 151. CS 261 and CS 271 require CS 152. CS 351 requires CS 152 and MATH 251W. DATA 252 requires DATA 152. MATH 280 requires Calculus.

Notes

  1. CS 151 is required MATH 251W, CS 152, DATA 351, and DATA 352W.
  2. DATA 151 is required for DATA 152. It is not required for MATH 138.
  3. Statistics and DATA 151 are required for DATA 252. CS 370 which requires CS 151 is required for CS 475.
  4. There are no remaining elective requirements.
B.S. Computer Science and B.S. Mathematics, (15 Courses) [Coming Soon]

3+1 BS/MS Programs

B.S. Data Science and M.S. Data Science, (6 Courses [Undergraduate] + 8 Courses [Graduate/Professional]) [Show] B.S. Data Science and M.S. Data Science, (6 Courses [Undergraduate] + 8 Courses [Graduate/Professional]) [Hide]

Distribution

  • 2 courses in Data Science, 1 if taking Statistics outside Data Science
  • 1 course in Computer Science
  • 1 course in Mathematics, 2 if taking Statistics in Mathematics
  • 2 electives in Data Science, Computer Science, or otherwise approved
  • 8 graduate/professional courses in Data Science, Computer Science, or otherwise approved

Requirements (Undergraduate)

  1. Statistics Requirement, choose one of
      

    • DATA 152 : Inferential Statistics

    • MATH 138 : Introduction to Applied Statistics
  2. DATA 151 : Intro to Data Science in R

  3. CS   151 : Intro to Programming in Python

  4. MATH 280 : Math for Data Science*

  5. Elective

  6. Elective
*Requires calculus or instructor consent.

Notes

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 and 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 are considered undergraduates for three years of two semesters, then graduate/professional students for one year of three semesters.

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
B.S. Computer and Data Science and M.S. Data Science, (9 Courses [Undergraduate] + 8 Courses [Graduate/Professional]) [Show] B.S. Computer and Data Science and M.S. Data Science, (9 Courses [Undergraduate] + 8 Courses [Graduate/Professional]) [Hide]

Distribution

  • 2 courses in Data Science, 1 if taking Statistics outside Data Science
  • 5 course in Computer Science
  • 2 course in Mathematics, 3 if taking Statistics in Mathematics
  • 8 graduate/professional courses in Data Science, Computer Science, or otherwise approved

Requirements (Undergraduate)

  1. Statistics Requirement, choose one of
      

    • DATA 152 : Inferential Statistics

    • MATH 138 : Introduction to Applied Statistics
  2. DATA 151 : Intro to Data Science in R

  3. CS   151 : Intro to Programming in Python

  4. CS   152 : Data Structures

  5. MATH 251W: Foundations of Advanced Mathematics

  6. MATH 280 : Math for Data Science*

  7. CS   261 : Software Development

  8. CS   271 : Networks and Systems

  9. CS   351 : Analysis of Algorithms


*Requires calculus or instructor consent.

Notes

All notes as in the above degree programs, other than that there are no electives. DATA 504W counts toward the DATA 352W requirement for the B.S. Computer Science.

Proposed Minors (Subject to Change)

Minor in Statistics, (6 Courses) [Show] Minor in Statistics, (6 Courses) [Hide]

Distribution

  • 3 courses in Data Science, 2 if taking Statistics or Machine Learning outside Data Science, 1 if taking both outside
  • 2 courses in Mathematics, 3 if taking Statistics in Mathematics
  • 1 elective in Data Science, or MATH 352, ECON 350, BIOL 342, BIOL 347, SOC 341

Requirements (Undergraduate)

  1. Data Science Requirement, choose one of
    • 
DATA 151 : Intro to Data Science in R
  2. Statistics Requirement, choose one of
      

    • DATA 152 : Inferential Statistics

    • MATH 138 : Introduction to Applied Statistics
  3. Machine Learning Requirement, choose one of
      

    • DATA 252 : Models and Machine Learning

    • CS__ 475 : Machine Learning*
  4. Integral Calculus Requirement, choose one of
      

    • MATH 152 : Calculus II

    • MATH 249 : Multivariable Calculus
  5. Probability Requirement, choose one of
      

    • MATH 266 : Probability and Statistics

    • MATH 376 : Topics: Probability Theory*
  6. Elective
*Denotes classes with requirements that do not count toward the minor.

Notes

This is a proposed program and is subject to change. CS 475 requires CS 370 which requires CS 151. MATH 376 requires MATH 251W. AP Credit in Statistics and Calculus (BC) is accepted.

Minor in Applied Mathematics, (6 Courses) [Show] Minor in Applied Mathematics, (6 Courses) [Hide]

Distribution

  • 4 courses in Mathematics
  • 1 course in Computer Science
  • 1 elective listed here which includes any CS/DATA 200+.

Requirements

  1. MATH 249 : Multivariable Calculus

  2. MATH 251W: Foundations of Advanced Mathematics

  3. MATH 256 : Differential Equations
  4. Topics Requirement, choose one of
      

    • MATH 266 : Probability and Statistics

    • MATH 280 : Math for Data Science

    • MATH 352 : Linear Algebra

    • MATH 376 : Topics: Any
  5. CS   151 : Intro to Programming in Python

  6. Elective

Notes

This is a proposed program and is subject to change.