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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) |
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|---|---|---|
| CSE 1301 | Introduction to Computer Science (AP credit may satisfy this requirement) | 3 |
| CSE 2107 | Introduction to Data Science | 3 |
| CSE 2407 | Data Structures and Algorithms | 3 |
| CSE 3104 | Data Manipulation and Management | 3 |
|
or ESE 4170 or SDS 4430 |
Introduction to Machine Learning Introduction to Machine Learning and Pattern Classification Multivariate Statistical Analysis |
3 |
| Math 1510 | Calculus I (AP credit may satisfy this requirement) | 3 |
| Math 1520 | Calculus II (AP credit may satisfy this requirement) | 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 |
*Each of these core courses must be passed with a grade of C- or better.
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
Computer Science and Engineering
- CSE 2307 Programming Tools and Techniques
- CSE 2506 Introduction to Human-Centered Design
- CSE 3050 Responsible Data Science (Cannot be double-counted in EPR)
- CSE 3101 Introduction to Intelligent Agents Using Science Fiction
- CSE 3407 Analysis of Algorithms
- CSE 4061 Text Mining
- CSE 4101 AI and Society (Cannot be double-counted in EPR)
- CSE 4102 Intro to Artificial Intelligence
- CSE 4106 Data Science for Complex Networks
- CSE 4107 Introduction to Machine Learning (Cannot be double-counted in CR)
- CSE 4109 Introduction to AI for Health (Cannot be double-counted as Practicum)
- CSE 4207 Cloud Computing
- CSE 4305 Database Management Systems
- CSE 4507 Introduction to Visualization
- CSE 5104 Data Mining
- CSE 5105 Bayesian Methods in Machine Learning
- CSE 5107 Machine Learning
- CSE 5108 Human-in-the-Loop Computation
- CSE 5403 Algorithms for Nonlinear Optimization
- CSE 5509 Computer Vision
Statistics and Data Science
- SDS 3110 Biostatistics
- SDS 4020 Mathematical Statistics
- SDS 4110 Experimental Design
- SDS 4120 Survival Analysis
- SDS 4140 Advanced Linear Statistical Models
- SDS 4155 Time Series Analysis
- SDS 4210 Statistical Computation
- SDS 4310 Bayesian Statistics
- SDS 4430 Statistical Learning (Cannot be double-counted in CR)
- SDS 4440 Mathematical Foundations of Big Data
- SDS 4480 Topics in Statistics: Machine Learning Methods in Biological Sciences
- SDS 4720 Stochastics Processes
- SDS 5061 Theory Of Statistics I
- SDS 5062 Theory Of Statistics II
- SDS 5071 Advanced Linear Models I
- SDS 5072 Advanced Linear Models II
- SDS 5531 Advanced Statistical Computing I
- SDS 5532 Advanced Statistical Computing II
- SDS 5595 Topics in Statistics: Spatial Statistics
- SDS 5800 Topics in Statistics
- SDS 5805 Topics in Statistics
Mathematics
Electrical & Systems Engineering
Energy, Environmental & Chemical Engineering
- EECE 2020 Computational Modeling in Energy, Environmental and Chemical Engineering
Linguistics
- Ling 3250 Introduction to Computational Linguistics
Ethics and Professional Responsibility Requirement (EPR)
- 3 units of courses from the following list:
Ethics and Professional Responsibility (EPR)
| CSE 3050 | Responsible Data Science (Cannot be double-counted as an Elective) | 3 units |
| CSE 4101 | AI and Society (Cannot be double-counted as an Elective) | 3 units |
| Engr 4501 | Engineering Ethics and Sustainability | 1 unit |
| Engr 4502 | Engineering Leadership and Team Building | 1 unit |
| Engr 4503 | Conflict Management and Negotiation | 1 unit |
| MSB 5560 | Ethics in Biostatistics and Data Science | 2 units |
| Phil 2070 | Business Ethics | 3 units |
| Phil 3160 | Classical Ethical Theories | 3 units |
| Phil 4250 | Normative Ethical Theory | 3 units |
| PolSci 3313 | Theories of Social Justice | 3 units |
Practicum Requirement |
|---|
|
Additional Departmental Requirements |
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|---|---|---|
| CWP 1500 | College Writing I | 3 units |
| Engr 3100 | 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 |