The McKelvey School of Engineering and the College of Arts & Sciences developed a new major that efficiently captures the intersection of mathematics and statistics with computer science for data science. 

Data Science Core Requirements (CR)

Data Science Technical Electives

Four courses from Mathematics & Statistics or Computer Science & Engineering can be chosen from an approved list, with the following caveats:

• At least one course from Mathematics & Statistics (at the 400 level or above)
• At least one course from CSE (ending in S, T, M, or A)
• At most one course at the 200 level

List of Approved Electives 

Mathematics and Statistics and Data Science

Course List

Code	Title	Units
Math 449	Numerical Applied Mathematics	3
Math 450	Topics in Applied Mathematics	3
Math 456	Topics in Financial Mathematics	3
Math 4601	Statistical Learning (Cannot be double-counted in CR)	3
Math 5047	Geometry/Topology III: Differential Geometry	3
SDS 322	Biostatistics	3
SDS 420	Experimental Design	3
SDS 434	Survival Analysis	3
SDS 4392	Advanced Linear Statistical Models	3
SDS 459	Bayesian Statistics	3
SDS 460	Multivariate Statistical Analysis	3
SDS 461	Time Series Analysis	3
SDS 462	Mathematical Foundations of Big Data	3
SDS 475	Statistical Computation	3
SDS 493	Probability	3
SDS 494	Mathematical Statistics	3
SDS 495	Stochastic Processes	3
SDS 5061	Theory of Statistics I	3
SDS 5062	Theory of Statistics II	3
SDS 5071	Advanced Linear Models I	3
SDS 5072	Advanced Linear Models II	3

Computer Science & Engineering

Course List

Code	Title	Units
CSE 237S	Programming Tools and Techniques	3
CSE 256A	Introduction to Human-Centered Design	3
CSE 311A	Introduction to Intelligent Agents Using Science Fiction	3
CSE 347	Analysis of Algorithms	3
CSE 359A	Signals, Data and Equity (Cannot be double-counted in EPR)	3
CSE 411A	AI and Society (Cannot be double-counted in EPR)	3
CSE 412A	Introduction to Artificial Intelligence	3
CSE 416A	Data Science for Complex Networks	3
CSE 417T	Introduction to Machine Learning (Cannot be double-counted in CR)	3
CSE 427S	Cloud Computing with Big Data Applications	3
CSE 435S	Database Management Systems	3
CSE 457A	Introduction to Visualization	3
CSE 514A	Data Mining	3
CSE 515T	Bayesian Methods in Machine Learning	3
CSE 517A	Machine Learning	3
CSE 518A	Human-in-the-Loop Computation	3
CSE 534A	Large-Scale Optimization for Data Science	3
CSE 543T	Algorithms for Nonlinear Optimization	3
CSE 559A	Computer Vision	3

Electrical and Systems Engineering​

Course List

Code	Title	Units
ESE 4031	Optimization for Engineered Planning, Decisions and Operations	3
ESE 415	Optimization	3

Energy, Environmental & Chemical Engineering

Course List

Code	Title	Units
EECE 202	Computational Modeling in Energy, Environmental and Chemical Engineering	3

Linguistics 

Course List

Code	Title	Units
Ling 317	Introduction to Computational Linguistics	3

Ethics and Professional Responsibility Requirement (EPR)

• 3 units of courses from an approved list

​List of Approved Courses

Course List

Code	Title	Units
CSE 359A	Signals, Data and Equity (Cannot be double-counted as an Elective)	3
CSE 411A	AI and Society (Cannot be double-counted as an Elective)	3
Engr 4501	Engineering Ethics and Sustainability	1
Engr 4502	Engineering Leadership and Team Building	1
Engr 4503	Conflict Management and Negotiation	1
Engr 450F	Engineers in the Community (Engineering Ethics, Leadership and Conflict Management)	3
Engr 520P	Presentation Skills for Scientists and Engineers	2
MSB 512	Ethics in Biostatistics and Data Science	2

Practicum Requirement

• Students must complete 3 units of an approved comprehensive data science project or experience. The practicum must be approved by the committee of data science faculty.
• The practicum experience should be completed during the next-to-last semester of study (i.e., the first semester of the senior year). It is important that practicum plans be submitted for review prior to starting the project or course work to ensure the proposed work is sufficient for the objectives of the practicum. After-the-fact approvals are possible but not guaranteed.
• Appropriate practicum work is possible via Independent Study (CSE 400E or Math 400) or via project-focused classes, including (but not limited to) CSE 437S Software Engineering Workshop and CSE 454A Software Engineering for External Clients. Students should contact the course instructors in advance to identify the degree of agency the student will have over project selection and requirements. 
• Contact the CSE undergraduate coordinator in the CSE department office or the Math department office to initiate the approval process.

Contact Info