Data Science (CSE), BS

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. No courses may be double-counted; for example, a course used to fulfill a technical elective cannot also fulfill the practicum requirement, even if the course is listed in both categories below. 

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 College 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). All courses taken to meet any of the degree requirements cannot be taken on a Pass/No Pass basis; however, for McKelvey Engineering students, this restriction does not apply to humanities and social science electives. 

Data Science Core Requirements (CR)

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

Course List

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 4106	Data Science for Complex Networks	3
CSE 4107	Introduction to Machine Learning	3
CSE 4109	Introduction to AI for Health	3
CSE 4207	Cloud Computing With Big Data Applications	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

Course List

Code	Title	Units
SDS 3110	Biostatistics	3
SDS 4020	Mathematical Statistics	3
SDS 4070	Stochastic Processes	3
SDS 4110	Experimental Design	3
SDS 4120	Survival Analysis	3
SDS 4135	Applied Statistics Practicum	3
SDS 4140	Advanced Linear Statistical Models	3
SDS 4155	Time Series Analysis	3
SDS 4210	Statistical Computation	3
SDS 4425	Data Mining Methods and Applications	3
SDS 4310	Bayesian Statistics	3
SDS 4430	Statistical Learning	3
SDS 4440	Mathematical Foundations of Data Science	3
SDS 4480	Topics in Statistics	3
SDS 4971	Topics in Statistics: Data Mining	3
SDS 5521	Advanced Linear Models I	3
SDS 5522	Advanced Linear Models II	3
SDS 5525	Theory of Statistics I	3
SDS 5526	Theory of Statistics 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: Optimization Methods For Machine Learning	3

Mathematics 

Course List

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 and Systems Engineering

Course List

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

Course List

Code	Title	Units
EECE 2020	Computational Modeling in Energy, Environmental and Chemical Engineering	3

Linguistics 

Course List

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

Course List

Code	Title	Units
CSE 3050	Responsible Data Science	3
CSE 4101	AI and Society	3
ENGR 2170	Historical and Philosophical Aspects of Science, Engineering and Technology	3
PHIL 2070	Business Ethics	3
PHIL 3015	Philosophy of Artificial Intelligence	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.
• We recommend to complete the practicum experience prior to the last semester of study. 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 Independent Study or SDS 4000 Undergraduate Independent Study) 
  • Project-focused courses, including (but not limited to) CSE 4109 Introduction to AI for Health, CSE 4307 Software Engineering Workshop, CSE 4504 Software Engineering for External Clients, and SDS 4135 Applied Statistics Practicum. 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) and the Academic Coordinator in the Department of Statistics and Data Science. 

Contact Info