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 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 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 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
  • 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. 
  • Contact Maria Sanchez (smaria@wustl.edu) 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 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