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. The Bachelor of Science in Data Science (BSDS) will give students the formal foundation needed to understand the applicability and consequences of the various approaches to analyzing data with a focus on statistical modeling and machine learning.
McKelvey Engineering students who declare this major must fulfill the core course requirements listed below and all other requirements for the Applied Science degree in the McKelvey School of Engineering. They must also complete Engr 310 Technical Writing and 8 units of courses designated as NSM (Natural Sciences & Math) from Anthropology (L48 Anthro), Biology and Biomedical Sciences (L41 Biol), Chemistry (L07 Chem), Earth and Planetary Sciences (L19 EPSc), Physics (L31 Physics) or Environmental Studies (L82 EnSt).
Arts & Sciences students who declare this major must fulfill the distribution requirements and all other requirements for an AB degree in addition to the specific requirements listed below.
Data Science Core Requirements (CR)*
Course List | Code | Title | Units |
| Math 131 | Calculus I | 3 |
| Math 132 | Calculus II | 3 |
| Math 233 | Calculus III | 3 |
| Math 309 | Matrix Algebra | 3 |
| Math 3211 | Statistics for Data Science I | 3 |
| Math 4211 | Statistics for Data Science II | 3 |
| Math 439 | Linear Statistical Models | 3 |
| CSE 131 | Introduction to Computer Science | 3 |
| CSE 247 | Data Structures and Algorithms | 3 |
| CSE 217A | Introduction to Data Science | 3 |
| CSE 314A | Data Manipulation and Management | 3 |
| CSE 417T | Introduction to Machine Learning (or Math 4601 Statistical Learning) | 3 |
| Total Units | 36 |
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 Data Science Technical Electives
Computer Science and 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 | Analysis of Network Data | 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 |
Mathematics and Statistics
Electrical and Systems Engineering
Course List | Code | Title | Units |
| ESE 4031 | Optimization for Engineered Planning, Decisions and Operations | 3 |
| ESE 415 | Optimization | 3 |
| ESE 427 | Financial Mathematics | 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 the following list:
List of EPR Course Options
Course List | Code | Title | Units |
| 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 |
| 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 |
| MSB 512 | Ethics in Biostatistics and Data Science | 2 |
Practicum Requirement
- 3 units of an approved comprehensive data science project or experience. A 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 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 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.