Bachelor of Science in Data Science
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 Bachelor of Arts in Data Science (BADS) 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, Environmental, and Planetary Sciences (L19 EEPS), 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)
| Code | Title | Units |
|---|---|---|
| Math 131 | Calculus I | 3 |
| Math 132 | Calculus II | 3 |
| Math 233 | Calculus III | 3 |
| Math 309 | Matrix Algebra | 3 |
| SDS 3211 | Statistics for Data Science I | 3 |
| SDS 4211 | Statistics for Data Science II | 3 |
| SDS 439 | Linear Statistical Models | 3 |
| CSE 131 | Introduction to Computer Science | 3 |
| CSE 217A | Introduction to Data Science | 3 |
| CSE 247 | Data Structures and Algorithms | 3 |
| CSE 314A | Data Manipulation and Management | 3 |
| CSE 417T | Introduction to Machine Learning | 3 |
| or SDS 460 | Multivariate Statistical Analysis | |
| Total Units | 36 | |
Notes:
- 1.
Each of these core courses must be passed with a grade of C- or better.
- 2.
AP credit can be applied for Math 131 Calculus I, Math 132 Calculus II, and Math 233 Calculus III. Students who have completed Math 203 Honors Mathematics I and Math 204 Honors Mathematics II will have this requirement waived.
- 3.
CSE 131 Introduction to Computer Science may be waived after consultation with the director of undergraduate studies of the Department of Computer Science and Engineering.
Data Science Technical Electives
Four courses can be chosen from the list of approved electives given below, with the following caveats:
- At least one course from Statistics and Data Science (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
| 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 |
Statistics and Data Science
| Code | Title | Units |
|---|---|---|
| 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 494 | Mathematical Statistics | 3 |
| SDS 495 | Stochastic Processes | 3 |
| SDS 496 | Topics in Statistics | 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 579 | Topics in Statistics | 3 |
| SDS 586 | Topics in Statistics | 3 |
Mathematics
| Code | Title | Units |
|---|---|---|
| Math 449 | Numerical Applied Mathematics | 3 |
| Math 450 | Topics in Applied Mathematics | 3 |
| Math 456 | Topics in Financial Mathematics | 3 |
| Math 5047 | Geometry/Topology III: Differential Geometry | 3 |
Electrical and Systems Engineering
| 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
| Code | Title | Units |
|---|---|---|
| EECE 202 | Computational Modeling in Energy, Environmental and Chemical Engineering | 3 |
Linguistics
| 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
| Code | Title | Units |
|---|---|---|
| Engr 450F | Engineers in the Community (Engineering Ethics, Leadership and Conflict Management) | 3 |
| Engr 4501 | Engineering Ethics and Sustainability | 1 |
| Engr 4502 | Engineering Leadership and Team Building | 1 |
| Engr 4503 | Conflict Management and Negotiation | 1 |
| Engr 520P | Presentation Skills for Scientists and Engineers | 2 |
| CSE 359A | Signals, Data and Equity (This course 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
- 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 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 SDS 400 Undergraduate Independent Study) 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.
- Internships related to data science can be used to fulfill the practicum. If the internship is paid, it cannot count for credit, but can satisfy the requirements.
- 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 Associate Chair in the Statistics and Data Science Department, José Figueroa-López.