Bachelor of Science in Data Science

The McKelvey School of Engineering and the College of Arts & Sciences developed a new major that efficiently captures the intersection of mathematics and statistics with computer science for data science. The Bachelor of Science in Data Science (BSDS) will give students 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. 

McKelvey Engineering students who declare this major must fulfill the core course requirements listed below and all other requirements for the Applied Science degree in the McKelvey School of Engineering. They must also complete Engr 310 Technical Writing and 8 units of courses designated as NSM (Natural Sciences & Math) from Anthropology (L48 Anthro), Biology and Biomedical Sciences (L41 Biol), Chemistry (L07 Chem), Earth and Planetary Sciences (L19 EPSc), Physics (L31 Physics) or Environmental Studies (L82 EnSt).

Arts & Sciences students who declare this major must fulfill the distribution requirements and all other requirements for an AB degree in addition to the specific requirements listed below.

Data Science Core Requirements (CR)

Math 131Calculus I3
Math 132Calculus II3
Math 233Calculus III3
Math 309Matrix Algebra3
Math 3211Statistics for Data Science I3
Math 4211Statistics for Data Science II3
Math 439Linear Statistical Models3
CSE 131Introduction to Computer Science3
CSE 247Data Structures and Algorithms3
CSE 217AIntroduction to Data Science3
CSE 314AData Manipulation and Management3
CSE 417TIntroduction to Machine Learning (or Math 4601 Statistical Learning)3
Total Units36

Data Science Technical Electives

Four courses from Mathematics & Statistics or Computer Science & Engineering can be chosen from an approved list, with the following caveats:

  • At least one course from Mathematics & Statistics (at the 400 level or above)
  • At least one course from CSE (ending in S, T, M, or A)
  • At most one course at the 200 level 

List of Approved Data Science Technical Electives

Computer Science and Engineering

CSE 237SProgramming Tools and Techniques3
CSE 256AIntroduction to Human-Centered Design3
CSE 311AIntroduction to Intelligent Agents Using Science Fiction3
CSE 347Analysis of Algorithms3
CSE 359ASignals, Data and Equity (Cannot be double-counted in EPR)3
CSE 411AAI and Society (Cannot be double-counted in EPR)3
CSE 412AIntroduction to Artificial Intelligence3
CSE 416AAnalysis of Network Data3
CSE 417TIntroduction to Machine Learning (Cannot be double-counted in CR)3
CSE 427SCloud Computing with Big Data Applications3
CSE 435SDatabase Management Systems3
CSE 457AIntroduction to Visualization3
CSE 514AData Mining3
CSE 515TBayesian Methods in Machine Learning3
CSE 517AMachine Learning3
CSE 518AHuman-in-the-Loop Computation3
CSE 534ALarge-Scale Optimization for Data Science3
CSE 543TAlgorithms for Nonlinear Optimization3
CSE 559AComputer Vision3

Mathematics and Statistics

Math 322Biostatistics3
Math 420Experimental Design3
Math 434Survival Analysis3
Math 4392Advanced Linear Statistical Models3
Math 449Numerical Applied Mathematics3
Math 450Topics in Applied Mathematics3
Math 456Topics in Financial Mathematics3
Math 459Bayesian Statistics3
Math 460Multivariate Statistical Analysis3
Math 461Time Series Analysis3
Math 4601 Statistical Learning (Cannot be double-counted in CR)3
Math 462Mathematical Foundations of Big Data3
Math 475Statistical Computation3
Math 493Probability3
Math 494Mathematical Statistics3
Math 495Stochastic Processes3
Math 5047Geometry/Topology III: Differential Geometry3
Math 5061Theory of Statistics I3
Math 5062Theory of Statistics II3
Math 5071Linear Statistical Models Grad3
Math 5072Advanced Linear Models II3

Electrical and Systems Engineering

ESE 4031Optimization for Engineered Planning, Decisions and Operations3
ESE 415Optimization3
ESE 427Financial Mathematics3

Energy, Environmental & Chemical Engineering

EECE 202Computational Modeling in Energy, Environmental and Chemical Engineering3

 Linguistics 

Ling 317Introduction to Computational Linguistics3

Ethics and Professional Responsibility Requirement (EPR)

  • 3 units of courses from the following list:

List of EPR Course Options

Engr 4501Engineering Ethics and Sustainability1
Engr 4502Engineering Leadership and Team Building1
Engr 4503Conflict Management and Negotiation1
Engr 450FEngineers in the Community (Engineering Ethics, Leadership and Conflict Management)3
Engr 520PPresentation Skills for Scientists and Engineers2
CSE 359ASignals, Data and Equity (Cannot be double-counted as an Elective)3
CSE 411AAI and Society (Cannot be double-counted as an Elective)3
MSB 512Ethics in Biostatistics and Data Science2

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 during the next-to-last semester of study (i.e., the first semester of 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 Clients. 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.