Data Science

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Minor in Data Science · 23 credits

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Typical path

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Technical Electives

Pick 6 credits · 26 courses to choose from

100s

None

200s

None

300s

400s

Linear Algebra

AMS 210Applied Linear Algebra3 cr
or
MAT 211Introduction to Linear Algebra3 cr
or
MAT 308Differential Equations with Linear Algebra4 cr

Catalog text

AMS 210 - Applied Linear Algebra 3 credits OR MAT 211 - Introduction to Linear Algebra 3 credits OR MAT 308 - Differential Equations with Linear Algebra 4 credits

Required Courses

AMS 310Survey of Probability and Statistics3 cr
CSE 114Introduction to Object-Oriented Programming4 cr
CSE 214Data Structures4 cr
CSE 351Introduction to Data Science3 cr

Alternative path: replaces CSE 114, CSE 214

CSE 160Computer Science A: Honors3 cr
CSE 161Laboratory for Computer Science A: Honors1 cr
CSE 260Computer Science B: Honors3 cr
CSE 261Laboratory for Computer Science B: Honors1 cr

Catalog text

Students may substitute CSE 160, CSE 161, CSE 260, and CSE 261 for CSE 114 and CSE 214.

Catalog text

AMS 310 - Survey of Probability and Statistics 3 credits; CSE 114 - Introduction to Object-Oriented Programming 4 credits; CSE 214 - Data Structures 4 credits; CSE 351 - Introduction to Data Science 3 credits

Technical Electives

Choose 6 credits

AMS 301Finite Mathematical Structures3 cr
AMS 311Probability Theory3 cr
AMS 315Data Analysis3 cr
CSE 357Statistical Methods for Data Science3 cr
AMS 316Introduction to Time Series Analysis3 cr
AMS 317Introduction to Linear Regression Analysis3 cr
AMS 326Numerical Analysis3 cr
AMS 341Operations Research I: Deterministic Models3 cr
AMS 345Computational Geometry3 cr
CSE 355Computational 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 305Principles of Database Systems3 cr
ISE 305Database Design and Practice3 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
CSE 487Research in Computer Science0–3 cr
CSE 488Internship in Computer Science3 cr

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

No more than one of each: AMS 315 / CSE 357; CSE 305 / ISE 305; AMS 345 / CSE 355.

Catalog text

At least six credits of the following courses. At most three credits of AMS 487, AMS 488, CSE 487 or CSE 488 may be used to satisfy the technical elective requirement. AMS 301; AMS 311; AMS 315 OR CSE 357; AMS 316; AMS 317; AMS 326; AMS 341; AMS 345 OR CSE 355; AMS 380; AMS 394; AMS 412; AMS 487; AMS 488; CSE 305 OR ISE 305; CSE 327; CSE 332; CSE 337; CSE 352; CSE 353; CSE 354; CSE 373; CSE 487; CSE 488. Notes: CSE 357 and AMS 315 may not both be taken to satisfy minor requirements. CSE 305 and ISE 305 may not both be taken to satisfy minor requirements. CSE 355 and AMS 345 may not both be taken to satisfy minor requirements.

Credits not shared with major or other minors

At least 15 credits taken to satisfy the minor in Data Science may not also be used to satisfy a student's majors or other minors requirements.

Catalog text

At least 15 credits taken to satisfy the minor in Data Science may not also be used to satisfy a student's majors or other minors requirements.

Program rules

Letter grades
Courses in “Linear Algebra”, “Required Courses”, “Technical Electives” must be taken for a letter grade.
Minimum grade
C or higher in “Linear Algebra”, “Required Courses”, “Technical Electives”.

Notes

Admission: To declare the minor, students must complete CSE 114 and either AMS 210 or AMS 310 with a GPA of 3.0 or higher in these courses. Priority is given to students with a cumulative GPA of 3.0 or higher. For students who have completed more than two courses applicable towards minor entry, the GPA is computed using the highest grades earned in two of the courses. At most one of the courses used to meet minor entry requirements may be repeated. Only courses transferred before matriculation can be used for DAS minor admission purposes. Admission is competitive and contingent upon program capacity.
Admission: The minor is open to all students not majoring in Computer Science or Data Science, or minoring in Computer Science or Information Systems.
Advising: Students who have declared the minor should see a Data Science Undergraduate Advisor to discuss a suitable selection of electives.