The MS in Applied and Computational Mathematics provides students with a rigorous, modern training in applied and computational mathematics and in the mathematics of data. The program is targeted to students with an undergraduate degree in mathematics or other quantitative disciplines such as computer science, statistics, economics and engineering. Through foundational and advanced coursework, students gain a strong combination of quantitative and computational skills as well as data fluency, positioning them for careers in industry or for advanced studies. Students can satisfy the 30-credit requirement in 12 to 24 months, with accelerated paths supported by relevant summer course offerings. Graduates are well-prepared for roles in information technology, finance, engineering, research, and education – particularly within the rapidly growing sectors of machine learning and artificial intelligence – or to pursue a PhD in the mathematical, statistical, and computational sciences.
Admissions
Please consult the table below for key information about this degree program’s admissions requirements. The program may have more detailed admissions requirements, which can be found below the table or on the program’s website.
Graduate admissions is a two-step process between academic programs and the Graduate School. Applicants must meet the minimum requirements of the Graduate School as well as the program(s). Once you have researched the graduate program(s) you are interested in, apply online.
| Fall Deadline | March 15 |
| Spring Deadline | November 1 |
| Summer Deadline | This program does not admit for summer |
| GRE (Graduate Record Examinations) | Not required but may be considered if available. |
| English Proficiency Test | Refer to the Graduate School: Minimum Requirements for Admission Policy: https://policy.wisc.edu/library/UW-1241 |
| Other Test (s) (e.g., GMAT, MCAT) | The GRE subject test in Mathematics is not required but may be considered if available. |
| Letters of Recommendation | 3 |
The program is aimed at students with an undergraduate degree in mathematics or a related quantitative field. Prior coursework in multivariable calculus and linear algebra is required. Prior coursework in differential equations, probability, programming, and introduction to proofs is recommended.
Admissions Materials
- Three letters of recommendation
- Unofficial transcripts: If you are recommended for admission, the Graduate School will reach out to request official transcripts at that time.
- Mathematics coursework: In table format, list all undergraduate and graduate mathematics courses you have completed or are currently enrolled in. For each course, provide the following information: name of the school where it was taken, course number, title, description, textbooks used, and grade you received (for completed courses).
- Resume
- Statement of purpose: Explain your motivations for pursuing graduate studies, and convey how your interests/experiences/goals align with the program.
Funding
Graduate School Resources
The Bursar’s Office provides information about tuition and fees associated with being a graduate student. Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid. Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and restrictions related to funding.
Minimum Graduate School Requirements
Review the Graduate School minimum degree requirements and policies, in addition to the program requirements listed below.
Major Requirements
Mode of Instruction
| Face to Face | Evening/Weekend | Online | Hybrid | Accelerated |
|---|---|---|---|---|
| Yes | No | No | No | Yes |
Mode of Instruction Definitions
Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.
Evening/Weekend: Courses meet on the UW–Madison campus only in evenings and/or on weekends to accommodate typical business schedules. Students have the advantages of face-to-face courses with the flexibility to keep work and other life commitments.
Face-to-Face: Courses typically meet during weekdays on the UW-Madison Campus.
Hybrid: These programs combine face-to-face and online learning formats. Contact the program for more specific information.
Online: These programs are offered 100% online. Some programs may require an on-campus orientation or residency experience, but the courses will be facilitated in an online format.
Curricular Requirements
| Minimum Credit Requirement | 30 credits |
| Minimum Residence Credit Requirement | 16 credits |
| Minimum Graduate Coursework Requirement | 24 credits must be graduate-level coursework. Refer to the Graduate School's Minimum Graduate Coursework (50%) Requirement Policy: https://policy.wisc.edu/library/UW-1244 |
| Overall Graduate GPA Requirement | 3.00 GPA required. Refer to the Graduate School: Grade Point Average (GPA) Requirement Policy:https://policy.wisc.edu/library/UW-1203 |
| Other Grade Requirements | None |
| Assessments and Examinations | None |
| Language Requirements | None |
Required Courses
| Code | Title | Credits |
|---|---|---|
| Core | ||
| Students must complete 18 credits from the following Core categories. At least 6 credits must be completed from each category. At least two courses must be numbered 700 or higher. | ||
| Theory and Modeling | 6 | |
| Applied Mathematical Analysis 2: Partial Differential Equations | ||
| Applied Dynamical Systems, Chaos and Modeling | ||
| Introduction to Stochastic Processes | ||
| Methods of Applied Mathematics 1 | ||
| Methods of Applied Mathematics-2 | ||
| Computational Methods | 6 | |
| Numerical Linear Algebra | ||
| Numerical Analysis | ||
| Methods of Computational Mathematics I | ||
| Methods of Computational Mathematics II | ||
| Stochastic Computational Methods | ||
| Mathematical Data Science | 6 | |
| Graphs and Networks in Data Science | ||
| Mathematical Methods in Data Science | ||
| Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | ||
| Stochastic Computational Methods | ||
| Randomized Linear Algebra and Applications | ||
| Electives | ||
| Students must complete at least 12 additional credits from the lists above or below. At most 6 credits can be taken from List B. At most one MATH course can be taken in coursework numbered 800-899. | 12 | |
| Introduction to the Theory of Probability | ||
| Ordinary Differential Equations | ||
| Analysis I | ||
| Analysis II | ||
| Probability Theory | ||
| Mathematical Methods for Systems Biology | ||
| Analysis of Partial Differential Equations | ||
| Complex Analysis | ||
| Introduction to Fourier Analysis | ||
| Introduction to Measure and Integration | ||
| An Introduction to Brownian Motion and Stochastic Calculus | ||
| Mathematical Fluid Dynamics | ||
| Ordinary Differential Equations | ||
| Partial Differential Equations | ||
| Partial Differential Equations | ||
| A First Course in Real Analysis | ||
| Complex Analysis | ||
| A Second Course in Real Analysis | ||
| Theory of Probability I | ||
| Theory of Probability II | ||
| Stochastic Analysis | ||
| Topics in Applied Mathematics | ||
| Topics in Mathematical Data Science | ||
| List B | ||
| Programming II | ||
| Programming III | ||
| Introduction to Optimization | ||
| Nonlinear Optimization I | ||
| Nonlinear Optimization II | ||
| Machine Learning | ||
| Mathematical Foundations of Machine Learning | ||
| Linear Optimization | ||
| Statistical Learning | ||
| Mathematical Statistics I | ||
| Mathematical Statistics II | ||
| Computational Statistics | ||
| Bayesian Statistics | ||
| Advanced Statistical Methods | ||
| Data and Algorithms: Ethics and Policy | ||
| Computational Methods in Electromagnetics | ||
| High Performance Computing for Applications in Engineering | ||
| Total Credits | 30 | |
Other Policy
Students in this program may not take courses outside the prescribed curriculum without faculty advisor and program director approval. Students in this program cannot enroll concurrently in other undergraduate or graduate degree programs.
Policies
Graduate School Policies
The Graduate School’s Academic Policies and Procedures serve as the official document of record for Graduate School academic and administrative policies and procedures and are updated continuously. Note some policies redirect to entries in the official UW-Madison Policy Library. Programs may set more stringent policies than the Graduate School. Policies set by the academic degree program can be found below.
Major-Specific Policies
Prior Coursework
Graduate Credits Earned at Other Institutions
Graduate coursework from other institutions will be evaluated on a case-by-case basis. With the approval of the director, students in the MS program may transfer no more than 7 credits of graduate coursework from other institutions. Coursework earned ten or more years prior to admission to a master's degree is not allowed to satisfy requirements.
Undergraduate Credits Earned at Other Institutions or UW-Madison
Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
Credits Earned as a Professional Student at UW-Madison (Law, Medicine, Pharmacy, and Veterinary careers)
Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
Credits Earned as a University Special Student at UW-Madison
Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
Probation
Refer to the Graduate School: Probation policy.
Advisor/Committee
Refer to the Graduate School: Advisor and Graduate School: Committees (Doctoral/Master's/MFA) policies.
Credits Per Term Allowed
15 credit maximum. Refer to the Graduate School: Maximum Credit Loads and Overload Requests policy.
Time Limits
Refer to the Graduate School: Time Limits policy.
Grievances and Appeals
These resources may be helpful in addressing your concerns:
- Bias or Hate Reporting
- Graduate Assistantship Policies and Procedures
- Hostile and Intimidating Behavior Policies and Procedures
- Employee Assistance (for personal counseling and workplace consultation around communication and conflict involving graduate assistants and other employees, post-doctoral students, faculty and staff)
- Employee Disability Resource Office (for qualified employees or applicants with disabilities to have equal employment opportunities)
- Graduate School (for informal advice at any level of review and for official appeals of program/departmental or school/college grievance decisions)
- Office of Compliance (for class harassment and discrimination, including sexual harassment and sexual violence)
- Office Student Assistance and Support (OSAS) (for all students to seek grievance assistance and support)
- Office of Student Conduct and Community Standards (for conflicts involving students)
- Ombuds Office for Faculty and Staff (for employed graduate students and post-docs, as well as faculty and staff)
- Title IX (for concerns about discrimination)
L&S Policy for Graduate Student Academic Appeals
Graduate students have the right to appeal an academic decision related to an L&S graduate program if the student believes that the decision is inconsistent with published policy.
Academic decisions that may be appealed include:
- Dismissal from the graduate program
- Failure to pass a qualifying or preliminary examination
- Failure to achieve satisfactory academic progress
- Academic disciplinary action related to failure to meet professional conduct standards
Issues such as the following cannot be appealed using this process:
- A faculty member declining to serve as a graduate student’s advisor.
- Decisions regarding the student’s disciplinary knowledge, evaluation of the quality of work, or similar judgements. These are the domain of the department faculty.
- Course grades. These can be appealed instead using the L&S Policy for Grade Appeal.
- Incidents of bias or hate, hostile and intimidating behavior, or discrimination (Title IX, Office of Compliance). Direct these to the linked campus offices appropriate for the incident(s).
Appeal Process for Graduate Students
A graduate student wishing to appeal an academic decision must follow the process in the order listed below. Note time limits within each step.
- The student should first seek informal resolution, if possible, by discussing the concern with their academic advisor, the department’s Director of Graduate Studies, and/or the department chair.
- If the program has an appeal policy listed in their graduate program handbook, the student should follow the policy as written, including adhering to any indicated deadlines. In the absence of a specific departmental process, the chair or designee will be the reviewer and decision maker, and the student should submit a written appeal to the chair within 15 business days of the academic decision. The chair or designee will notify the student in writing of their decision.
- If the departmental process upholds the original decision, the graduate student may next initiate an appeal to L&S. To do so, the student must submit a written appeal to the L&S Assistant Dean for Graduate Student Academic Affairs within 15 business days of notification of the department’s decision.
- To the fullest extent possible, the written appeal should include, in a single document: a clear and concise statement of the academic decision being appealed, any relevant background on what led to the decision, the specific policies involved, the relief sought, any relevant documentation related to the departmental appeal, and the names and titles of any individuals contributing to or involved in the decision.
- The Assistant Dean will work with the Academic Associate Dean of the appropriate division to consider the appeal. They may seek additional information and/or meetings related to the case.
- The Assistant Dean and Academic Associate Dean will provide a written decision within 20 business days.
- If L&S upholds the original decision, the graduate student may appeal to the Graduate School. More information can be found on their website: Grievances and Appeals (see: Graduate School Appeal Process).
Other
The Department of Mathematics cannot provide financial support for students in the MS in Applied and Computational Mathematics degree.
Professional Development
Graduate School Resources
Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career.
Learning Outcomes
- Describe and analyze key concepts from a substantial body of mathematics presented in introductory graduate-level courses in mathematics.
- Demonstrate knowledge of mathematical techniques used to solve problems motivated by applications.
- Identify and implement strategies to solve problems using mathematical modeling, computational methods and/or data-driven approaches.
- Communicate clearly in written and/or oral presentations.
- Recognize and apply principles of ethical and professional conduct.