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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
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 |
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|
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 |
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 |