Bachelor of Science 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 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.
Students who declare this major must fulfill the core course requirements and electives listed below. In addition, students need to meet the ethics and professional responsibility requirement as well as the practicum requirement. Arts & Sciences students who declare this major must fulfill all other requirements for a BA degree. McKelvey Engineering students who declare this major must complete all other requirements for the Applied Science degree in the McKelvey School of Engineering. They must also complete ENGR 3100 Technical Writing and 8 units of courses designated as Natural Sciences & Math (NSM) from Anthropology (ANTHRO), Biology and Biomedical Sciences (BIOL), Chemistry (CHEM), Earth, Environmental, and Planetary Sciences (EEPS), Physics (PHYSICS), or Environmental Studies (ENST).
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 Multivariate Statistical Analysis 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 the list of approved electives given below, 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 and Engineering
| Code | Title | Units |
|---|---|---|
| CSE 2307 | Programming Tools and Techniques | 3 |
| CSE 2506 | Introduction to Human Centered Design | 3 |
| CSE 3050* | Responsible Data Science | 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 | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107** | Introduction to Machine Learning | 3 |
| CSE 4109*** | Introduction to AI for Health | 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 |
- *
CSE 3050 and CSE 4101 cannot be double counted in EPR.
- **
CSE 4107 cannot be double counted in CR.
- ***
CSE 4109 cannot be double counted as Practicum.
Statistics and Data Science
| Code | Title | Units |
|---|---|---|
| SDS 3110 | Biostatistics | 3 |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 4110 | Experimental Design | 3 |
| SDS 4120 | Survival Analysis | 3 |
| SDS 4140 | Advanced Linear Statistical Models | 3 |
| SDS 4155 | Time Series Analysis | 3 |
| SDS 4210 | Statistical Computation | 3 |
| SDS 4310 | Bayesian Statistics | 3 |
| SDS 4430* | Statistical Learning | 3 |
| SDS 4440 | Mathematical Foundations of Data Science | 3 |
| SDS 4480 | Topics in Statistics, Machine Learning Methods in Biological Sciences | 3 |
| SDS 4720 | Stochastic Processes | 3 |
| SDS 5061 | Theory of Statistics I | 3 |
| SDS 5062 | Theory Statistics II | 3 |
| SDS 5071 | Advanced Linear Models | 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 |
- *
SDS 4430 cannot be double counted in CR.
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 | 3 |
Electrical and 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 the following list:
List of EPR Course Options
| Code | Title | Units |
|---|---|---|
| CSE 3050* | Responsible Data Science | 3 |
| CSE 4101* | AI and Society | 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 |
- *
CSE 3050 and CSE 4101 cannot be double counted as technical electives.
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 courses, 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 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.