Decorative image

The Department of Computer Sciences (CS) offers a dynamic environment for study, research, and professional growth.

The MS in Data Engineering program focuses on the principles and practices of managing data at scale. It emphasizes the valid and efficient collection, storage, management, and processing of datasets to support computation and data driven systems important to data science and data analytics functions. Given the increasing amounts of data being generated and processed daily, almost all industries need data engineers to build and maintain robust data-handling systems. There is a strong workforce demand for data engineering expertise.

Visit the department website for faculty interests, research activities, courses, and additional program information. Students may also be interested in other programs offered by the Department of Computer Sciences including:

  • Computer Sciences Master's Program (MS Computer Sciences: Computer Sciences) - A research oriented master’s degree that prepares students for careers in industry research or for PhD level education in Computer Sciences.
  • Professional Master's Program (MS Computer Sciences: Professional Program) - This degree is designed for students who are primarily interested in a professional career as computer scientist in a variety of industries. 

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 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 Every applicant whose native language is not English or whose undergraduate instruction was not in English must provide an English proficiency test score and meet the Graduate School minimum requirements (
Other Test(s) (e.g., GMAT, MCAT) n/a
Letters of Recommendation Required 3 required

For the Fall 2022 term, applications will be accepted until July 15, 2022.


Applicants to the MS Data Engineering program should have completed a bachelor's degree in computer science or a related field.

Graduate School Resources

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.

Minimum Graduate School Requirements

Review the Graduate School minimum academic progress and degree requirements, in addition to the program requirements listed below.

Major Requirements


Face to Face Evening/Weekend Online Hybrid Accelerated
Yes No Yes Yes Yes

Mode of Instruction Definitions

Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students are able to complete a program with minimal disruptions to careers and other commitments.

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.


Minimum Credit Requirement 30 credits
Minimum Residence Credit Requirement 16 credits
Minimum Graduate Coursework Requirement 15 credits must be graduate-level coursework. Details can be found in the Graduate School’s Minimum Graduate Coursework (50%) policy (
Overall Graduate GPA Requirement 3.00 GPA required.
This program follows the Graduate School's policy:
Other Grade Requirements None.
Assessments and Examinations None.
Language Requirements None.

 Required Courses

Data Engineering Foundations: complete all classes12
Distributed Systems
Big Data Systems
Topics in Database Management Systems
Topics in Computing 1
Machine Learning Requirement: select a minimum of 2 courses from the list below6
Introduction to Artificial Intelligence
Machine Learning
Advanced Deep Learning
Introduction to Machine Learning and Statistical Pattern Classification
Introduction to Deep Learning and Generative Models
Statistical Learning
Algorithms Requirement: select a minimum of one class from below3
Introduction to Optimization
Introduction to Algorithms
Nonlinear Optimization I
Systems Requirement: select a minimum of one class from below3
Foundations of Mobile Systems and Applications
Introduction to Operating Systems
Database Management Systems: Design and Implementation
Introduction to Computer Networks
Mobile and Wireless Networking
Advanced Computer Networks
Humans and Data Requirement: select a minimum of one class from below3
Data Visualization
Human-Computer Interaction
Approved Electives: select any course from above or from the list below3
Introduction to Information Security
Graduate Cooperative Education 2
Master's Thesis 2
Master's Research 2
Advanced Seminar in Computer Science 2
Statistical Models for Data Science
Statistical Inference for Data Science
Statistical Methods for Data Science
Total Credits30

Specific offerings of COMP SCI 838 Topics in ComputingCOMP SCI 838 Topics in Computing are counted as fulfilling the Data Engineering Core requirement only with approval of the Graduate Advising Committee.


COMP SCI 799 Master's ResearchCOMP SCI 799 Master's Research, COMP SCI 790 Master's Thesis, COMP SCI 702 Graduate Cooperative Education, and COMP SCI 900 Advanced Seminar in Computer Science can be taken for a combined total of at most three elective credits.

  • Courses used as an elective cannot also be used to fulfill data engineering fundamentals requirements or breadth requirements for machine learning, algorithms, systems, and humans and data.

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, graduate or certificate programs.

Graduate School Policies

The Graduate School’s Academic Policies and Procedures provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.


Major-Specific Policies


Graduate Work from Other Institutions

With program approval, students are allowed to count 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 master’s degree is not allowed to satisfy requirements.

UW–Madison Undergraduate

With program approval, up to 7 STAT credits from a UW–Madison undergraduate degree are allowed to count toward minimum graduate degree credits. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.

UW–Madison University Special

With program approval, up to 15 STAT credits completed at UW–Madison while a University Special student at the 300 level or above are allowed to count toward minimum graduate degree and graduate residence credit requirements. Of these credits, those at the 700 level or above may also count toward the minimum graduate coursework (50%) requirement. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.


This program follows the Graduate School's Probation policy.


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


15 credits


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.


These resources may be helpful in addressing your concerns:

Students should contact the department chair or program director with questions about grievances. They may also contact the L&S Academic Divisional Associate Deans, the L&S Associate Dean for Teaching and Learning Administration, or the L&S Director of Human Resources.


Not applicable.

Graduate School Resources

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

Program Resources

The Department of Computer Sciences hosts many professional development opportunities including: job fairs, workshops, seminars, talks, employer information sessions, mentoring, and student socials.  The Department of Computer Science’s student organizations, Student-ACM (SACM) and Women’s ACM (WACM), are active partners in providing professional development opportunities for computer sciences graduate students.

  1. Design, implement and evaluate the use of analytic algorithms on sample datasets.
  2. Explain how a machine-learning model is developed for and evaluated on real world datasets.
  3. Design and execute experimental data collection and processing, and present resulting analyses using best practices in human-centered data communications.
  4. Apply and customize analytics, systems and human-centered techniques to application-specific data engineering requirements and objectives.
  5. Identify tradeoffs among data engineering techniques (analytics, systems and/or human-centered) and contrast design alternatives, within the context of specific data engineering application domains.
  6. Survey, interpret and comparatively criticize state of the art data engineering research talks and papers,with emphasis on constructive improvements.
  7. Organize, execute, report on, and present a real world data engineering project in collaboration with other researchers/programmers.

Visit the CS website to view our department faculty and staff.