Program Requirements
- Total Units Required: 51-54
- Grade Requirement: All courses must be passed with a grade of C– or better
Required Courses and Practicum
- 12 core requirement courses (36 credits)
- Four elective courses (12 credits)
- A course in Ethics and Professional Responsibility (3 credits)
- Practicum requirement (3 credits if completed via independent study or a project-focused course)
- We will allow undergraduates to take any SDS 5000-level courses that are not listed as undergraduate courses.
The College of Arts & Sciences and McKelvey School of Engineering developed a new major that efficiently captures the intersection of mathematics and statistics with computer science for data science. The Bachelor of Arts in Data Science (BADS) 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.
Students who declare this major must fulfill the core course requirements and electives listed below. In addition, students need to meet the ethics and professional responsibility requirement as well as the practicum requirement. No courses may be double-counted; for example, a course used to fulfill a technical elective cannot also fulfill the practicum requirement, even if the course is listed in both categories below.
Arts & Sciences students who declare this major must fulfill all other requirements for a BA degree. McKelvey Engineering students who declare this major must complete all other requirements for the Applied Science degree in the McKelvey School of Engineering. They must also complete College Writing and 8 units of courses designated as Natural Sciences & Math (NSM) from Anthropology (ANTHRO); Biology and Biomedical Sciences (BIOL); Chemistry (CHEM); Earth, Environmental, and Planetary Sciences (EEPS); Physics (PHYSICS); or Environmental Studies (ENST). All courses taken to meet any of the degree requirements cannot be taken on a Pass/No Pass basis; however, for McKelvey Engineering students, this restriction does not apply to humanities and social science electives.
Data Science Core Requirements (CR)
| Code | Title | Units |
|---|---|---|
| CSE 1301 | Introduction to Computer Science | 3 |
| CSE 2107 | Introduction to Data Science | 3 |
| CSE 2407 | Data Structures and Algorithms | 3 |
| CSE 3104 | Data Manipulation and Management | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| or ESE 4170 | Introduction to Machine Learning and Pattern Classification | |
| or SDS 4430 | Statistical Learning | |
| MATH 1510 | Calculus I | 3 |
| MATH 1520 | Calculus II | 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 |
| Total Units | 36 | |
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
List of Approved Data Science Technical Electives
Computer Science and Engineering
| Code | Title | Units |
|---|---|---|
| CSE 2307 | Programming Tools and Techniques | 3 |
| CSE 2506 | Introduction to Human-Centered Design | 3 |
| CSE 3050 | Responsible Data Science | 3 |
| CSE 3101 | Introduction to Intelligent Agents Using Science Fiction | 3 |
| CSE 3407 | Analysis of Algorithms | 3 |
| CSE 4061 | Text Mining | 3 |
| CSE 4101 | AI and Society | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4106 | Data Science for Complex Networks | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 4109 | Introduction to AI for Health | 3 |
| CSE 4207 | Cloud Computing With Big Data Applications | 3 |
| CSE 4305 | Database Management Systems | 3 |
| CSE 4507 | Introduction to Visualization | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5108 | Human-In-The-Loop Computation | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5509 | Computer Vision | 3 |
Statistics and Data Science
| Code | Title | Units |
|---|---|---|
| SDS 3110 | Biostatistics | 3 |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 4070 | Stochastic Processes | 3 |
| SDS 4110 | Experimental Design | 3 |
| SDS 4120 | Survival Analysis | 3 |
| SDS 4135 | Applied Statistics Practicum | 3 |
| SDS 4140 | Advanced Linear Statistical Models | 3 |
| SDS 4155 | Time Series Analysis | 3 |
| SDS 4210 | Statistical Computation | 3 |
| SDS 4425 | Data Mining Methods and Applications | 3 |
| SDS 4310 | Bayesian Statistics | 3 |
| SDS 4430 | Statistical Learning | 3 |
| SDS 4440 | Mathematical Foundations of Data Science | 3 |
| SDS 4480 | Topics in Statistics | 3 |
| SDS 4971 | Topics in Statistics: Data Mining | 3 |
| SDS 5521 | Advanced Linear Models I | 3 |
| SDS 5522 | Advanced Linear Models II | 3 |
| SDS 5525 | Theory of Statistics I | 3 |
| SDS 5526 | Theory of Statistics II | 3 |
| SDS 5531 | Advanced Statistical Computing I | 3 |
| SDS 5532 | Advanced Statistical Computing II | 3 |
| SDS 5595 | Topics in Statistics: Spatial Statistics | 3 |
| SDS 5800 | Topics in Statistics: Optimization Methods For Machine Learning | 3 |
Mathematics
| Code | Title | Units |
|---|---|---|
| MATH 4501 | Numerical Applied Mathematics | 3 |
| MATH 4502 | Topics in Applied Mathematics | 3 |
| MATH 4560 | Topics in Financial Mathematics | 3 |
| MATH 5223 | Geometry/Topology III: Differential Geometry | 3 |
Electrical and Systems Engineering
| Code | Title | Units |
|---|---|---|
| ESE 3590 | Signals, Data and Equity | 3 |
| ESE 4031 | Optimization for Engineered Planning, Decisions and Operations | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 4270 | Financial Mathematics | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science | 3 |
Energy, Environmental & Chemical Engineering
| Code | Title | Units |
|---|---|---|
| EECE 2020 | Computational Modeling in Energy, Environmental and Chemical Engineering | 3 |
Linguistics
| Code | Title | Units |
|---|---|---|
| LING 3250 | Introduction to Computational Linguistics | 3 |
Ethics and Professional Responsibility Requirement (EPR)
- 3 units of courses from the following list:
List of EPR Course Options
| Code | Title | Units |
|---|---|---|
| CSE 3050 | Responsible Data Science | 3 |
| CSE 4101 | AI and Society | 3 |
| ENGR 2170 | Historical and Philosophical Aspects of Science, Engineering and Technology | 3 |
| PHIL 2070 | Business Ethics | 3 |
| PHIL 3015 | Philosophy of Artificial Intelligence | 3 |
| PHIL 3160 | Classical Ethical Theories | 3 |
| PHIL 4250 | Normative Ethical Theory | 3 |
| POLSCI 3313 | Theories of Social Justice | 3 |
Practicum Requirement
- Students must complete an approved comprehensive data science project or experience for their practicum requirement. The practicum must be approved by the committee of data science faculty.
- We recommend to complete the practicum experience prior to the last semester of study. It is important that practicum plans be submitted for review prior to starting the project or coursework 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 the following pathways:
- Independent Study (CSE 4001 Independent Study or SDS 4000 Undergraduate Independent Study)
- Project-focused courses, including (but not limited to) CSE 4109 Introduction to AI for Health, CSE 4307 Software Engineering Workshop, CSE 4504 Software Engineering for External Clients, and SDS 4135 Applied Statistics Practicum. Students should contact course instructors in advance to identify the degree of agency the student will have over project selection and requirements.
- Internships related to data science can be used to fulfill the practicum. Internships (paid or unpaid) cannot count for credit, but they can satisfy the practicum requirement.
- To initiate the approval process, majors through the McKelvey School of Engineering should contact the CSE undergraduate coordinator in the CSE department, and majors through Arts & Sciences should contact the Undergraduate Director(s) and the Academic Coordinator in the Department of Statistics and Data Science.
AP Credit, Waivers, and Course Substitutions
AP credit can be applied for MATH 1510 Calculus I and MATH 1520 Calculus II.
CSE 1301 Introduction to Computer Science may be waived with approval from the Director of Undergraduate Studies of the Department of Computer Science and Engineering.
Aside from the approved cases listed below, course substitutions will be considered on a case-by-case basis.
- ESE 3260 Probability and Statistics for Engineering may be substituted for SDS 3020 Elementary to Intermediate Statistics and Data Analysis.
- If both MATH 2801 Honors Mathematics I and MATH 2802 Honors Mathematics II are taken, they can be substituted for the entire calculus sequence MATH 1510 Calculus I, MATH 1520 Calculus II, and MATH 2130 Calculus III.
Course Transfer
Courses transferred from other accredited colleges and universities can be counted with department approval and with the following caveats:
- Courses transferred from a two-year college (e.g., a community college) cannot be used to satisfy upper-level requirements.
- Courses from WashU Continuing & Professional Studies cannot be used to fulfill minor or major requirements.
Distinctions in Data Science
Distinction
- For Distinction in Data Science, a student must take an additional two electives for a total of six electives.
- The student's GPA in the six electives must be at least 3.7. If the student takes additional courses that satisfy these requirements, the courses with the lowest grades may be omitted when calculating the GPA for this purpose.
- The electives need to be taken from the list of approved data science technical electives. Electives need to be passed with a grade of B or better.
High Distinction
- Complete all requirements for Distinction.
- Complete an honors thesis in either department (Statistics and Data Science or Computer Science & Engineering).
Highest Distinction
- Complete the requirements for High Distinction.
- Complete one of the two options described below:
- Qual Option: Take either SDS 5525 Theory of Statistics I–SDS 5526 Theory of Statistics II or SDS 5521 Advanced Linear Models I–SDS 5522 Advanced Linear Models II, which corresponds to one of the qualifiers for the PhD in Statistics, earning a grade of B+ or better in each course.
- Course Option: Complete three additional electives from the list of approved data science technical electives for a total of 12 courses. As with Distinction, the student's GPA in the 12 electives must be at least 3.7 with the student earning a grade of B+ or better in each course, and additional courses beyond 12 can be disregarded when calculating the GPA. At least four of these 12 courses must be from the Department of Statistics and Data Science, and at least four must be from the Department of Computer Science & Engineering.
Latin Honors
At the time of graduation, the Department of Statistics and Data Science will recommend that a candidate receive Latin Honors (cum laude, magna cum laude, or summa cum laude) if that student has completed the department's requirements for High Distinction or Highest Distinction in Mathematics, including an Honors Thesis. The actual award of Latin Honors is managed by the College of Arts & Sciences.
The Honors Thesis
Arts & Sciences majors who want to be candidates for Latin Honors, High Distinction, or Highest Distinction must complete an honors thesis. Writing an honors thesis involves a considerable amount of independent work, reading, creating mathematics, writing a paper that meets acceptable professional standards, and making an oral presentation of the results.
Types of Projects
An honors thesis can take three forms:
- A thesis that presents significant work by the student on one or more nontrivial statistics or probability problems.
- A project in applied statistics that involves an in-depth analysis of a large data set. To do an honors thesis involving data analysis, it is usually necessary to have completed SDS 3020 Elementary to Intermediate Statistics and Data Analysis, SDS 4010 Probability , and SDS 4020 Mathematical Statistics (or SDS 3030 Statistics for Data Science I and SDS 4030 Statistics for Data Science II ) by the end of the junior year and to have the ability to work with statistical software such as SAS, R, or Python.
- A substantial expository paper that follows independent study on an advanced topic under the guidance of a department faculty member. Such a report would involve the careful presentation of ideas and the synthesis of materials from several sources.
Process and Suggested Timeline
Junior Year, Spring Semester
- Talk with a faculty advisor about possible projects.
- Complete the Honors Proposal Form and submit it to the student's faculty advisor, who will then submit the form and distinction specification information to the Director of Undergraduate Studies and the Academic Coordinator.
Senior Year
- By the end of January, provide the advisor with a draft abstract and outline of the paper.
- By the end of February, submit a rough draft, including an abstract, to the advisor.
- The student and the advisor should agree on a date that the writing will be complete and on a date and time for the oral presentation in mid-Spring semester.
Departmental Prizes
Each year, the department considers graduating majors for several departmental prizes and awards a prize to a junior. Recipients are recognized at an annual awards ceremony in April where graduating majors each receive a certificate and a set of honors cords to be worn as part of the academic dress at Commencement. Awards are noted on the student's permanent university record.
Contact Info
| Contact: | Joe Guinness |
| Email: | sdsundergraddirector@wustl.edu |
| Website: | https://sds.wustl.edu/ |