Second Major in Data Science (CSE)
The College of Arts & Sciences and McKelvey School of Engineering developed a new major that efficiently captures the intersection of mathematics and statistics with computer science for data science. The second major provides an opportunity to combine data science with another degree program.
Data Science Core Requirements (CR)
| Code | Title | Units |
|---|---|---|
| CSE 1301 | Introduction to Computer Science | 3 |
| CSE 2107 | Introduction to Data Science | 3 |
| CSE 2407 | Data Structures and Algorithms | 3 |
| CSE 3104 | Data Manipulation and Management | 3 |
| CSE 4107 Introduction to Machine Learning or SDS 4430 Statistical Learning or ESE 4170 Introduction to Machine Learning and Pattern Classification | 3 | |
| MATH 1510 | Calculus I | 3 |
| MATH 1520 | Calculus II | 3 |
| MATH 2130 | Calculus III | 3 |
| MATH 3300 | Matrix Algebra | 3 |
| SDS 3030 | Statistics for Data Science I | 3 |
| SDS 4030 | Statistics for Data Science II | 3 |
| SDS 4130 | Linear Statistical Models | 3 |
| Total Units | 36 | |
Data Science Technical Electives
Four courses can be chosen from an approved list, with the following caveats:
- At least one course from Statistics and Data Science (at the 4000 level or above)
- At least one course from Computer Science & Engineering (at the 4000 level or above)
- At most one course at the 2000 level
List of Approved Data Science Technical Electives
Computer Science & Engineering
| Code | Title | Units |
|---|---|---|
| CSE 2307 | Programming Tools and Techniques | 3 |
| CSE 2506 | Introduction to Human-Centered Design | 3 |
| CSE 3050 | Responsible Data Science (Cannot be double-counted in EPR) | 3 |
| CSE 3101 | Introduction to Intelligent Agents Using Science Fiction | 3 |
| CSE 3407 | Analysis of Algorithms | 3 |
| CSE 4061 | Text Mining | 3 |
| CSE 4101 | AI and Society (Cannot be double-counted in EPR) | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4106 | Data Science for Complex Networks | 3 |
| CSE 4107 | Introduction to Machine Learning (Cannot be double-counted in CR) | 3 |
| CSE 4190 | Introduction to AI for Health (Cannot be double-counted as Practicum) | 3 |
| CSE 4207 | Cloud Computing | 3 |
| CSE 4305 | Database Management Systems | 3 |
| CSE 4507 | Introduction to Visualization | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5108 | Human-In-The-Loop Computation | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5509 | Computer Vision | 3 |
Statistics and Data Science
| Code | Title | Units |
|---|---|---|
| SDS 3110 | Biostatistics | 3 |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 4061 | Time Series Analysis | 3 |
| SDS 4096 | Topics in Statistics | 3 |
| SDS 4110 | Experimental Design | 3 |
| SDS 4120 | Survival Analysis | 3 |
| SDS 4140 | Advanced Linear Statistical Models | 3 |
| SDS 4210 | Statistical Computation | 3 |
| SDS 4310 | Bayesian Statistics | 3 |
| SDS 4430 | Statistical Learning (Cannot be double-counted in CR) | 3 |
| SDS 4440 | Mathematical Foundations of Data Science | 3 |
| SDS 4720 | 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 |
| SDS 5531 | Advanced Statistical Computing I | 3 |
| SDS 5532 | Advanced Statistical Computing II | 3 |
| SDS 5595 | Topics in Statistics: Spatial Statistics | 3 |
| SDS 5800 | Topics in Statistics | 3 |
| SDS 5805 | Topics in Statistics | 3 |
Mathematics
| Code | Title | Units |
|---|---|---|
| MATH 4501 | Numerical Applied Mathematics | 3 |
| MATH 4502 | Topics in Applied Mathematics | 3 |
| MATH 4560 | Topics in Financial Mathematics | 3 |
| MATH 5223 | Geometry/Topology III: Differential Geometry | 3 |
Electrical & Systems Engineering
| Code | Title | Units |
|---|---|---|
| ESE 3590 | Signals, Data and Equity | 3 |
| ESE 4031 | Optimization for Engineered Planning, Decisions and Operations | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 4270 | Financial Mathematics | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science | 3 |
Energy, Environmental, & Chemical Engineering
| Code | Title | Units |
|---|---|---|
| EECE 2020 | Computational Modeling in Energy, Environmental and Chemical Engineering | 3 |
Linguistics
| Code | Title | Units |
|---|---|---|
| LING 3250 | Introduction to Computational Linguistics | 3 |
Ethics and Professional Responsibility Requirement (EPR)
- 3 units of courses from an approved list
List of Approved Courses
| Code | Title | Units |
|---|---|---|
| CSE 3050 | Responsible Data Science (Cannot be double-counted as Technical Elective) | 3 |
| CSE 4101 | AI and Society (Cannot be double-counted as Technical Elective) | 3 |
| ENGR 4501 | Engineering Ethics and Sustainability | 1 |
| ENGR 4502 | Engineering Leadership and Team Building | 1 |
| ENGR 4503 | Conflict Management and Negotiation | 1 |
| MSB 5560 | Ethics in Biostatistics and Data Science | 2 |
| PHIL 2070 | Business Ethics | 3 |
| PHIL 3160 | Classical Ethical Theories | 3 |
| PHIL 4250 | Normative Ethical Theory | 3 |
| POLSCI 3313 | Theories of Social Justice | 3 |
Practicum Requirement
- Students must complete an approved comprehensive data science project or experience for their practicum requirement. 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 senior year). It is important that practicum plans be submitted for review prior to starting the project or coursework 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 the following pathways:
- Independent Study (CSE 4001 or SDS 4000)
- Project-focused classes, including (but not limited to) CSE 4109 Introduction to AI for Health, CSE 4307 Software Engineering Workshop, and CSE 4504 Software Engineering for External Clients. Students should contact course instructors in advance to identify the degree of agency the student will have over project selection and requirements.
- Internships related to data science can be used to fulfill the practicum. Internships (paid or unpaid) cannot count for credit, but they can satisfy the practicum requirement.
- To initiate the approval process, majors through the McKelvey School of Engineering should contact the CSE undergraduate coordinator in the CSE department, and majors through Arts & Sciences should contact the Undergraduate Director(s) in the Department of Statistics and Data Science.