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 Bachelor of Science in Data Science
Undergraduate Programs
Bachelor of Science in Data Science
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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 
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 HumanCentered Design
 CSE 311A Introduction to Intelligent Agents Using Science Fiction
 CSE 347 Analysis of Algorithms
 CSE 359A Signals, Data and Equity (Cannot be doublecounted in EPR)
 CSE 411A AI and Society (Cannot be doublecounted in EPR)
 CSE 412A Intro to AI
 CSE 416A Analysis of Network Data
 CSE 417T Introduction to Machine Learning (Cannot be doublecounted 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 534A LargeScale 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 doublecounted in CR)
 Math 462 Foundations of Big Data
 Math 475 Statistical Computation
 Math 493 Probability
 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 doublecounted as an Elective) 3 units E81 CSE 411A AI and Society (Cannot be doublecounted as an Elective) 3 units M21 MSB 512 Ethics in Biostatistics and Data Science 2 units
DS Practicum Requirement 


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 