Undergraduate Programs

Bachelor of Science in Data Science

Students majoring in data science will have 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. They will have the computing skills needed to ingest, manage and visualize data.

Additionally, they will be able to program on their own and use common libraries to implement solutions to solve data problems. They will have the experience of applying their knowledge and skills in a practical research or industrial setting.

This program of study is a collaboration between the Department of Mathematics and Statistics in Arts & Sciences and the Department of Computer Science & Engineering in the McKelvey School of Engineering.

Core Course Requirements (CR)
Math 131	Calculus I (AP credit may satisfy this requirement)	3
Math 132	Calculus II (AP credit may satisfy this requirement)	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 or Math 4601	Introduction to Machine Learning Statistical Learning	3

Each of these core courses must be passed with a grade of C- or better.

Electives (4 courses)

*At least one from Math/Statistics (400 or above)

*At least one from CSE (ending in S, T, M, or A)

*At most one course at the 200 level

Computer Science and Engineering

CSE 237S Programming Tools and Techniques
CSE 256A Introduction to Human-Centered Design
CSE 311A Introduction to Intelligent Agents Using Science Fiction
CSE 347 Analysis of Algorithms
CSE 359A Signals, Data and Equity (Cannot be double-counted in EPR)
CSE 411A AI and Society (Cannot be double-counted in EPR)
CSE 412A Intro to AI
CSE 416A Analysis of Network Data
CSE 417T Introduction to Machine Learning (Cannot be double-counted in CR)
CSE 427S Cloud Computing
CSE 435S Database Management Systems
CSE 457A Introduction to Visualization
CSE 514A Data Mining
CSE 515T Bayesian methods in ML
CSE 517A Machine Learning
CSE 518A Crowdsourcing Computing
CSE 527A Natural Language Processing
CSE 527S Data-driven Privacy and Security
CSE 534A Large-Scale Optimization for Data Science
CSE 543T Algorithms for Nonlinear Optimization
CSE 559A Computer Vision

Mathematics and Statistics

Math 322 Bio stats
Math 420 Experimental Design
Math 434 Survival Analysis
Math 4392 Advanced Linear Statistical Models
Math 449 Numerical Applied Mathematics
Math 450 Topics in Applied Mathematics
Math 456 Financial Mathematics
Math 459 Bayesian Statistics
Math 460 Statistical Learning (also possible in core)
Math 461 Time Series Analysis
Math 4601 Statistical Learning (Cannot be double-counted in CR)
Math 462 Foundations of Big Data
Math 475 Statistical Computation
Math 494 Mathematical Statistics
Math 495 Stochastic Processes
Math 5047 Diff Geometry
Math 5061 Theory Of Statistics I
Math 5062 Theory Of Statistics II
Math 5071 Advanced Linear Model I
Math 5072 Advanced Linear Model II

Electrical & Systems Engineering

ESE 4031 Optimization for Engineered Planning, Decisions and Operations
ESE 415 Optimization
ESE 427 Financial Mathematics

Energy, Environmental & Chemical Engineering

EECE 202 Computational Modeling in Energy, Environmental and Chemical Engineering

Linguistics

Ling 317 Introduction to Computational Linguistics

Ethics and Professional Responsibility (EPR) 3 units

Ethics and Professional Responsibility (EPR)

E60 Engr 4501	Engineering Ethics and Sustainability	1 unit
E60 Engr 4502	Engineering Leadership and Team Building	1 unit
E60 Engr 4503	Conflict Management and Negotiation	1 unit
E60 Engr 450F	Engineers in the Community (Engineering Ethics, Leadership and Conflict Management)	3 units
E60 Engr 520P	Presentation Skills for Scientists and Engineers	2 units
E81 CSE 359A	Signals, Data and Equity (Cannot be double-counted as an Elective)	3 units
E81 CSE 411A	AI and Society (Cannot be double-counted as an Elective)	3 units
M21 MSB 512	Ethics in Biostatistics and Data Science	2 units

DS 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 the next-to-last semester of study (i.e., first semester 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 Client. Students should contact course instructors in advance to identify the degree of agency the student will have over project selection and requirements. Download the practicum form here and contact the Undergraduate Coordinator in the CSE department office or the Math department office to initiate the approval process.

DS 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 the next-to-last semester of study (i.e., first semester 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 Client. Students should contact course instructors in advance to identify the degree of agency the student will have over project selection and requirements.
Download the practicum form here and contact the Undergraduate Coordinator in the CSE department office or the Math department office to initiate the approval process.

Additional Departmental Requirements
CWP 100	College Writing I	3 units
Engr 310	Technical Writing	3 units
Natural Sciences electives		8 units
Humanities and Social Sciences electives		18 units
Total Units		32

*The College Writing and Humanities and Social Sciences requirements are those required of all students in the McKelvey School of Engineering. The Natural Sciences requirement is for 8 units designated NSM (Natural Sciences and Mathematics) from any of the following departments: Anthropology; Biology; Chemistry; Earth, Environmental, and Planetary Sciences; Environmental Studies; or Physics. The College Writing and Natural Sciences courses must be completed with a grade of C- or better.

All courses taken to meet any of the above requirements (with the exception of the humanities and social sciences electives) cannot be taken on a pass/fail basis.

Sample Schedule starting in Year 1

Fall

Spring

Year 1

Math 131

CSE 131

Math 132

CSE 247

Year 2

Math 233

CSE 217A

Math 309

CSE 314A

Year 3

Math 3211

DS Elective 1

Ethics Course

Math 4211

Math 439

DS Elective 2

Year 4

CSE 417T (or Math 4601)

Practicum

DS Elective 3

DS Elective 4

Sample schedule starting in Year 2 (having credit already for Math 131 and CSE 131)

	Fall	Spring
Year 2	Math 132 CSE 247	Math 233 CSE 217A
Year 3	Math 309 Math 3211 CSE 314A	Math 4211 Math 439 Elective 1
Year 4	CSE 417T (or Math 460) Practicum Ethics	Elective 2 Elective 3 Elective 4