Program Overview

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Data Science, MS

The MS Data Science is a joint professional program between the Statistics and Computer Sciences Departments and is administered by the Statistics Department. The program provides students with abilities in computational and statistical thinking and skills, which may be combined with domain knowledge to address data-rich problems from diverse fields and various industries. Graduates will acquire data science competencies to think critically about data, and to manage, process, model, and analyze data to obtain meaning and knowledge, and further to use data in responsible, ethical ways. The curriculum addresses emerging and rapidly growing areas of applied statistical and computing research and practice. Graduates seek employment as data analysts and data scientists or pursue further education in data science, statistics, computer science, or related quantitative and computational fields.

Admissions

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: February 15
• Spring Deadline: The program does not admit in the spring.
• Summer Deadline: The program does not admit in the summer.
• GRE (Graduate Record Examinations): Not required.
• English Proficiency Test: Refer to the Graduate School: Minimum Requirements for Admission policy.
• Other Test(s) (e.g., GMAT, MCAT): n/a
• Letters of Recommendation Required: 2

Requisites for Admission

Applicants to the MS Data Science program should have completed the following courses equivalent to the UW-Madison courses listed below:

• Calculus and Mathematical Foundation, complete all below:
  • MATH 221: Calculus and Analytic Geometry 1 (5 credits)
  • MATH 222: Calculus and Analytic Geometry 2 (4 credits)
  • MATH 340: Elementary Matrix and Linear Algebra (3 credits)
  • or MATH 345: Linear Algebra and Optimization
• Programming Foundation, select one from the list below:
  • COMP SCI 220: Data Science Programming I (4 credits)
  • COMP SCI 300: Programming II (3 credits)
  • COMP SCI 320: Data Science Programming II (4 credits)
• Recommended previous coursework of significant experience in R:
  • STAT 303, STAT 304, & STAT 305: R for Statistics I, and R for Statistics II, and R for Statistics III (3 credits)
  • STAT 433: Data Science with R (3 credits)

Funding

Graduate School Resources

The Bursars 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.

Program Information

Students enrolled in this program are not eligible to receive tuition remission from graduate assistantship appointments at this institution. Additional information about funding for MS Data Science is available on the program website.

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

• Mode of Instruction: Face to Face, Evening/Weekend, Online, Hybrid, Accelerated
  • Face to Face: Yes
  • Evening/Weekend: No
  • Online: No
  • Hybrid: No
  • Accelerated: 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 UWMadison 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: 15 credits must be graduate-level coursework.
• Overall Graduate GPA Requirement: 3.00 GPA required.
• Other Grade Requirements: None.
• Assessments and Examinations: None.
• Language Requirements: No language requirements.

Required Courses

• Statistics Core:
  • STAT 611: Statistical Models for Data Science (3 credits)
  • STAT 612: Statistical Inference for Data Science (3 credits)
  • STAT 613: Statistical Methods for Data Science (3 credits)
• Computer Sciences Core:
  • Complete 1 course from each category for a total of 9 credits
  • Algorithms:
    • COMP SCI/E C E/I SY E 524: Introduction to Optimization
    • COMP SCI 577: Introduction to Algorithms
    • COMP SCI/I SY E/MATH/STAT 726: Nonlinear Optimization I
  • Systems:
    • COMP SCI 537: Introduction to Operating Systems
    • COMP SCI 544: Introduction to Big Data Systems
    • COMP SCI 564: Database Management Systems: Design and Implementation
    • COMP SCI 640: Introduction to Computer Networks
    • COMP SCI 642: Introduction to Information Security
    • COMP SCI 739: Distributed Systems
    • COMP SCI 744: Big Data Systems
    • COMP SCI 764: Topics in Database Management Systems
  • Humans and Data:
    • COMP SCI 765: Data Visualization
    • COMP SCI/ED PSYCH/PSYCH 770: Human-Computer Interaction
• Machine Learning Core:
  • Complete 2 courses from the list below for a total of 6 credits
  • COMP SCI 540: Introduction to Artificial Intelligence
  • COMP SCI/E C E 760: Machine Learning
  • COMP SCI/E C E 761: Mathematical Foundations of Machine Learning
  • COMP SCI 762: Advanced Deep Learning
  • STAT 451: Introduction to Machine Learning and Statistical Pattern Classification
  • STAT 453: Introduction to Deep Learning and Generative Models
  • STAT 615: Statistical Learning
• Data Science Electives:
  • Complete 6 credits from the courses below
  • COMP SCI/E C E/I SY E 524: Introduction to Optimization
  • COMP SCI 537: Introduction to Operating Systems
  • COMP SCI 544: Introduction to Big Data Systems
  • COMP SCI 564: Database Management Systems: Design and Implementation
  • COMP SCI/B M I 576: Introduction to Bioinformatics
  • COMP SCI 577: Introduction to Algorithms
  • COMP SCI 640: Introduction to Computer Networks
  • COMP SCI 642: Introduction to Information Security
  • COMP SCI 702: Graduate Cooperative Education
  • COMP SCI/I SY E/MATH/STAT 726: Nonlinear Optimization I
  • COMP SCI 736: Advanced Operating Systems
  • COMP SCI 739: Distributed Systems
  • COMP SCI 744: Big Data Systems
  • COMP SCI/E C E 763: Trustworthy Artificial Intelligence
  • COMP SCI 764: Topics in Database Management Systems
  • COMP SCI 765: Data Visualization
  • COMP SCI/E C E 766: Computer Vision
  • COMP SCI 769: Advanced Natural Language Processing
  • COMP SCI/ED PSYCH/PSYCH 770: Human-Computer Interaction
  • COMP SCI 774: Data Exploration, Cleaning, and Integration for Data Science
  • COMP SCI 784: Foundations of Data Management
  • COMP SCI 799: Master's Research (3 credits maximum of COMP SCI 799 and/or STAT 699 allowed)
  • COMP SCI/E C E/STAT 861: Theoretical Foundations of Machine Learning
  • L I S 461: Data and Algorithms: Ethics and Policy
  • STAT 303, STAT 304, & STAT 305: R for Statistics I, and R for Statistics II, and R for Statistics III
  • STAT 349: Introduction to Time Series
  • STAT 351: Introductory Nonparametric Statistics
  • STAT/COMP SCI 403: Internship Course in Comp Sci and Data Science
  • STAT 411: An Introduction to Sample Survey Theory and Methods
  • STAT 421: Applied Categorical Data Analysis
  • STAT 433: Data Science with R
  • STAT 443: Classification and Regression Trees
  • STAT 456: Applied Multivariate Analysis
  • STAT 461: Financial Statistics
  • STAT/COMP SCI 471: Introduction to Computational Statistics
  • STAT 575: Statistical Methods for Spatial Data
  • STAT/B M I 620: Statistics in Human Genetics
  • STAT 699: Directed Study (3 credits maximum of STAT 699 and/or COMP SCI 799 allowed)
  • STAT 701: Applied Time Series Analysis, Forecasting and Control I
  • STAT 760: Multivariate Analysis I
  • STAT 761: Decision Trees for Multivariate Analysis
  • STAT 771: Computational Statistics
  • STAT/ECON/GEN BUS 775: Bayesian Statistics
  • I SY E 620: Simulation Modeling and Analysis
  • I SY E 624: Stochastic Modeling Techniques
  • I SY E/COMP SCI 719: Stochastic Programming
  • I SY E/COMP SCI 723: Dynamic Programming and Associated Topics
  • I SY E/COMP SCI/MATH 728: Integer Optimization
  • MATH 616: Data-Driven Dynamical Systems, Stochastic Modeling and Prediction
• Total Credits: 30

Graduate School Policies

The Graduate Schools 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

With program approval, students are allowed to transfer no more than 9 credits of graduate coursework from other institutions toward the graduate degree credit and graduate coursework (50%) requirements. Coursework earned five or more years prior to admission to a masters degree is not allowed to satisfy requirements.

Undergraduate Credits Earned at Other Institutions or UW-Madison

With program approval, up to 7 credits from a UWMadison undergraduate degree are allowed to transfer toward minimum graduate degree credits. Coursework earned five or more years prior to admission to a masters degree is not allowed to satisfy requirements. This program does not accept undergraduate credits from other institutions.

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 UWMadison

With program approval, up to 14 credits completed at UWMadison while a University Special student numbered 300 or above are allowed to transfer toward minimum graduate degree requirements. Of these credits, those numbered 700 or above may also transfer to fulfill the minimum graduate coursework (50%) requirement. Coursework earned five or more years prior to admission to a masters degree is not allowed to satisfy requirements.

Probation

Refer to the Graduate School: Probation policy.

Advisor / Committee

Students are required to communicate with their advisor near the beginning of each semester to discuss course selection and progress.

Credits Per Term Allowed

15 credit maximum. Refer to the Graduate School: Maximum Credit Loads and Overload Requests policy.

Time Limits

Students are expected to complete the program in 3-4 semesters. Students who wish to pursue the program part time must receive permission from the program chair.

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
• Office of the Provost for Faculty and Staff Affairs
• 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 students advisor.
• Decisions regarding the students 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.

1. The student should first seek informal resolution, if possible, by discussing the concern with their academic advisor, the departments Director of Graduate Studies, and/or the department chair.
2. 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.
3. 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 departments 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.

4. 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).

Professional Development

Graduate School Resources

Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career.

Program Resources

Students in the Data Science, MS program are encouraged to participate in program-specific professional development events and work directly, one-on-one, with advisors as well. Information about events and resources will be made available to currently enrolled students via email.

Learning Outcomes

1. Demonstrates understanding of theories, methodologies, and computation as tools to solve complex problems in data science.
2. Selects or adapts appropriate data science approaches and uses or develops best practices in data-driven applications.
3. Synthesizes information, organizes insights, and evaluates impact pertaining to questions for studies involving empirical data.
4. Communicates data science concepts and results clearly.
5. Adheres to principles of ethical and professional conduct in data science.