Data Science, BS

All programs

Bachelor of Science in Data Science · 58 credits

Catalog page

Typical path

+ First Year Seminar

+ SBC

+ First Year Seminar

+ SBC

+ Additional Calculus or Computer Science Foundations

+ SBC

+ Elective ×2

+ Linear Algebra Course (STEM+)

+ SBC ×2

+ Elective

+ Specialization Course

+ UD Elective

+ Elective

+ SBC

+ UD Technical Elective

+ Specialization Course

+ Elective

+ UD Technical Elective

+ UD Specialization Course

+ UD Elective ×2

+ UD Data Analysis Course

+ UD Technical Elective

+ UD Specialization Course

+ UD Elective ×2

+ SBC

All options

Hide courses not offered in

Technical Electives

Pick 9 credits · 2617 courses to choose from

Too many to list: the 24 listed courses

SpecializationBusiness1 group of options

Pick 3 courses

5 courses to choose from

100s

None

200s

None

300s

400s

None

SpecializationComputer Science1 group of options

Pick 12 credits

54 courses to choose from

100s

None

200s

None

300s

400s

SpecializationApplied Mathematics and Statistics1 group of options

Pick 12 credits

33 courses to choose from

100s

None

200s

None

300s

400s

1.Mathematics

One of
- All of
  - AMS 151Applied Calculus I3 cr
  - AMS 161Applied Calculus II3 cr
- or
  All of
  - MAT 125Calculus A3 cr
  - MAT 126Calculus B3 cr
  - MAT 127Calculus C3 cr
- or
  All of
  - MAT 131Calculus I4 cr
  - MAT 132Calculus II4 cr
- AMS 210Applied Linear Algebra3 cr
- or
  MAT 211Introduction to Linear Algebra3 cr
- or
  MAT 308Differential Equations with Linear Algebra4 cr
- AMS 261Applied Calculus III4 cr
- or
  CSE 113Foundations of Computer Science I4 cr
- or
  MAT 203Calculus III with Applications4 cr
- or
  MAT 307Multivariable Calculus with Linear Algebra4 cr
AMS 310Survey of Probability and Statistics3 cr

Catalog text

1. Mathematics: a. AMS 151 - Applied Calculus I 3 credits; b. AMS 161 - Applied Calculus II 3 credits; Note: The following alternate calculus course sequences may be substituted for AMS 151, AMS 161 in major requirements or prerequisites: MAT 125, MAT 126, MAT 127, or MAT 131, MAT 132. Equivalency for MAT courses achieved through the Mathematics Placement Examination is accepted to meet MAT course requirements. c. One Linear Algebra Course: AMS 210 - Applied Linear Algebra 3 credits; MAT 211 - Introduction to Linear Algebra 3 credits; MAT 308 - Differential Equations with Linear Algebra 4 credits. d. One Additional Calculus or Computer Science Foundations Courses: AMS 261 - Applied Calculus III 4 credits; CSE 113 - Foundations of Computer Science I 4 credits; MAT 203 - Calculus III with Applications 4 credits; MAT 307 - Multivariable Calculus with Linear Algebra 4 credits. e. AMS 310 - Survey of Probability and Statistics 3 credits

2.Computer Science

- CSE 101Computer Science Principles4 cr
- or
  CSE 103Data Science Principles4 cr
CSE 114Introduction to Object-Oriented Programming4 cr
CSE 214Data Structures4 cr

Catalog text

2. Computer Science: a. CSE 101 - Computer Science Principles 4 credits OR CSE 103 - Data Science Principles 4 credits; b. CSE 114 - Introduction to Object-Oriented Programming 4 credits; c. CSE 214 - Data Structures 4 credits

3.Data Science

CSE 351Introduction to Data Science3 cr
- AMS 315Data Analysis3 cr
- or
  CSE 357Statistical Methods for Data Science3 cr

Catalog text

3. Data Science: a. CSE 351 - Introduction to Data Science 3 credits; b. One Upper-Division Data Analysis Course: AMS 315 - Data Analysis 3 credits; CSE 357 - Statistical Methods for Data Science 3 credits

4.One Upper Division Writing Course

CSE 300Technical Communications3 cr

Catalog text

4. One Upper Division Writing Course: a. CSE 300 - Technical Communications 3 credits

5.Professional Ethics

CSE 312Social, Legal, and Ethical Issues in Computing3 cr

Catalog text

5. Professional Ethics: a. CSE 312 - Social, Legal, and Ethical Issues in Computing 3 credits

6.Technical Electives

Choose 9 credits

AMS 301Finite Mathematical Structures3 cr
AMS 311Probability Theory3 cr
AMS 316Introduction to Time Series Analysis3 cr
AMS 317Introduction to Linear Regression Analysis3 cr
AMS 318Financial Mathematics3 cr
AMS 326Numerical Analysis3 cr
AMS 341Operations Research I: Deterministic Models3 cr
AMS 345Computational Geometry3 cr
AMS 380Data Mining3 cr
AMS 394Statistical Laboratory3 cr
AMS 412Mathematical Statistics3 cr
AMS 487Research in Applied Mathematics and Statistics0–3 cr
AMS 488Internship in Applied Mathematics and Statistics0–3 cr
CSE 327Fundamentals of Computer Vision3 cr
CSE 332Introduction to Visualization3 cr
CSE 337Scripting Languages3 cr
CSE 352Artificial Intelligence3 cr
CSE 353Machine Learning3 cr
CSE 354Natural Language Processing3 cr
CSE 373Analysis of Algorithms3 cr
ISE 305Database Design and Practice3 cr
CSE 305Principles of Database Systems3 cr
CSE 487Research in Computer Science0–3 cr
CSE 488Internship in Computer Science3 cr

Excluding courses already counted toward requirement 3 (Data Science), requirement 7 (Specialization).

At most 3 credits from AMS 487, AMS 488, CSE 487, CSE 488.

Catalog text

6. Technical Electives: Each student must complete at least 9 credits of data science technical electives at the upper-division level. Electives may be drawn from any of the courses listed below. Other courses may be selected with approval of the Undergraduate Program Director. Notes: Courses taken to satisfy requirement 3 may not also be used to satisfy the technical elective requirement. Only 3 credits of AMS 487, AMS 488, CSE 487 or CSE 488 may be used to satisfy the technical elective requirement. a. AMS 301; b. AMS 311; c. AMS 316; d. AMS 317; e. AMS 318; f. AMS 326; g. AMS 341; h. AMS 345; i. AMS 380; j. AMS 394; k. AMS 412; l. AMS 487; m. AMS 488; n. CSE 327; o. CSE 332; p. CSE 337; q. CSE 352; r. CSE 353; s. CSE 354; t. CSE 373; u. One of: ISE 305 - Database Design and Practice 3 credits, CSE 305 - Principles of Database Systems 3 credits; v. CSE 487 - Research in Computer Science 0-3 credits; w. CSE 488 - Internship in Computer Science 3 credits

7.Specialization

One specialization

Business
All of
- BUS 220Introduction to Decision Sciences3 cr
- Choose 3 courses
  BUS 340Information Systems in Management3 cr
  BUS 346Operations Management3 cr
  BUS 375Introduction to Artificial Intelligence3 cr
  BUS 376Risk Management & Insurance3 cr
  BUS 377Risk Management & Insurance II3 cr
Computer Science
Choose 12 credits
- CSE courses numbered 300 and above
Excluding courses already counted toward requirement 1 (Mathematics), requirement 2 (Computer Science), requirement 3 (Data Science), requirement 4 (One Upper Division Writing Course), requirement 5 (Professional Ethics), requirement 6 (Technical Electives).
Applied Mathematics and Statistics
Choose 12 credits
- AMS courses numbered 300 and above
Excluding courses already counted toward requirement 1 (Mathematics), requirement 2 (Computer Science), requirement 3 (Data Science), requirement 4 (One Upper Division Writing Course), requirement 5 (Professional Ethics), requirement 6 (Technical Electives).

Catalog text

7. Specialization: Each student must complete a specialization consisting of 12 or more credits. Courses taken to satisfy a specialization requirement may not be used to satisfy the technical elective requirements. Business: BUS 220 - Introduction to Decision Sciences 3 credits; At least three of the following: BUS 340, BUS 346, BUS 375, BUS 376, BUS 377. Computer Science: Any combination of upper-division CSE technical lecture courses totaling 12 or more credits not taken to satisfy any other major requirement. Applied Mathematics and Statistics: Any combination of upper-division AMS technical lecture courses totaling 12 or more credits not taken to satisfy any other major requirement.

Total major credits

Completion of the major requires at least 58 credits.

Catalog text

The major in Data Science leads to the Bachelor of Science degree. Completion of the major requires at least 58 credits.

Additional Stony Brook residency

At least 15 additional major requirement credits must be completed at Stony Brook.

Catalog text

Residency requirement: 15 credits from requirements 3 and 6 below, and at least 15 additional major requirement credits must be completed at Stony Brook.

Sample schedule

Year 1 · Fall14 cr

First Year Seminar 1011
WRT 101Introductory Writing Workshop3
AMS 151Applied Calculus I3
CSE 101 / CSE 103Computer Science Principles4
SBC3

Year 1 · Spring14 cr

First Year Seminar 1021
WRT 102Intermediate Writing Workshop3
AMS 161Applied Calculus II3
CSE 114Introduction to Object-Oriented Programming4
SBC3

Year 2 · Fall17 cr

Additional Calculus or Computer Science Foundations4
CSE 214Data Structures4
SBC3
Elective3
Elective3

Year 2 · Spring15 cr

Linear Algebra Course (STEM+)3
AMS 310Survey of Probability and Statistics3
SBC3
SBC3
Elective3

Year 3 · Fall15 cr

CSE 312Social, Legal, and Ethical Issues in Computing3
Specialization Course 13
UD Elective3
Elective3
SBC3

Year 3 · Spring15 cr

CSE 300Technical Communications3
UD Technical Elective3
CSE 351Introduction to Data Science3
Specialization Course 23
Elective3

Year 4 · Fall15 cr

UD Technical Elective3
UD Specialization Course 33
UD Elective3
UD Elective3
UD Data Analysis Course3

Year 4 · Spring15 cr

UD Technical Elective3
UD Specialization Course 43
UD Elective3
UD Elective3
SBC3

Program rules

Letter grades
Courses in requirements 1, 2, 3, 4, 5, 6, 7 must be taken for a letter grade.
Minimum grade
C or higher in requirements 1, 2, 3, 4, 5, 6, 7.
Credits at Stony Brook
At least 15 credits in requirements 3, 6 must be taken at Stony Brook.

Stony Brook Curriculum

LANG exempt · CEAS majors are exempt from the LANG learning objective.

Notes

Admission: Acceptance into the major: Area of Interest or declared AMS, CSE, DAS and ISE students must complete CSE 114, CSE 214 and one of CSE 113, AMS 261, MAT 203 or MAT 307, with grades of B- or higher and a 3.00 GPA in those courses, repeat at most one of them, have a cumulative GPA of 3.00 or higher, and have course evaluations completed for transferred courses used toward the major. Requirements must be completed within one year of enrolling in the first course that applies toward major entry. Applications are due January 5 (Spring) and June 5 (Fall). Students outside AMS, CSE, DAS and ISE are admitted competitively, contingent on program capacity.
Advising: Students majoring in Data Science may not double-major or minor in Computer Science, Information Systems or Applied Mathematics & Statistics.
Note: The program offers a 'Data Science + X' option with a variety of subject areas through its specialization requirement.
Note: An accelerated BS in DAS + MS in DAS program is available to majors, in which nine credits may be applied to both undergraduate and graduate requirements. This optional program is not modeled here.