Program Overview

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Data Engineering, MS

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.

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: 3

Requisites for Admission

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

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.

Program Information

Mode of Instruction

• 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: None.

Required Courses

• Data Engineering Foundations:
  • Complete the following courses.
  • COMP SCI 739: Distributed Systems (3 credits)
  • COMP SCI 744: Big Data Systems (3 credits)
  • COMP SCI 764: Topics in Database Management Systems (3 credits)
  • COMP SCI 774: Data Exploration, Cleaning, and Integration for Data Science (3 credits)
• Machine Learning Requirement:
  • Complete a minimum of 2 courses from the list below (6 credits).
  • COMP SCI 540: Introduction to Artificial Intelligence
  • COMP SCI/E C E 760: 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
• Algorithms Requirement:
  • Complete a minimum of one course from below (3-4 credits).
  • 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 Requirement:
  • Complete a minimum of one course from below (3-4 credits).
  • COMP SCI 407: Foundations of Mobile Systems and Applications
  • COMP SCI 537: Introduction to Operating Systems
  • COMP SCI 564: Database Management Systems: Design and Implementation
  • COMP SCI 640: Introduction to Computer Networks
  • COMP SCI/E C E 707: Mobile and Wireless Networking
  • COMP SCI 740: Advanced Computer Networks
• Humans and Data Requirement:
  • Complete a minimum of one course from below (3 credits).
  • COMP SCI 765: Data Visualization
  • COMP SCI/ED PSYCH/PSYCH 770: Human-Computer Interaction
• Electives:
  • Complete any additional coursework from above or from the list below to meet 30 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.
  • COMP SCI 642: Introduction to Information Security
  • COMP SCI 702: Graduate Cooperative Education 1
  • COMP SCI 790: Master's Thesis 1
  • COMP SCI 799: Master's Research 1
  • COMP SCI 900: Advanced Seminar in Computer Science 1
  • STAT 611: Statistical Models for Data Science
  • STAT 612: Statistical Inference for Data Science
  • STAT 613: Statistical Methods for Data Science

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.

Major-Specific Policies

Prior Coursework

Graduate Credits Earned at Other Institutions

This program does not accept graduate transfer credits from other institutions.

Undergraduate Credits Earned at Other Institutions or UW-Madison

With program approval, up to 7 Statistics (STAT) credits from a UWMadison undergraduate degree are allowed to transfer for minimum graduate degree credits. Coursework earned ten or more years prior to admission to a masters degree is not allowed to satisfy requirements. This program does not accept undergraduate transfer 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 15 Statistics (STAT) credits completed at UWMadison while a University Special student numbered 300 or above are allowed to transfer for minimum graduate degree requirements.

Probation

Refer to the Graduate School: Probation policy.

Advisor / Committee

Students in the program will be assigned a faculty advisor and a staff advisor for purposes of advising.

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
• 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.

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

The Department of Computer Sciences hosts many professional development opportunities, including job fairs, workshops, seminars, talks, employer information sessions, mentoring, and student socials.

Learning Outcomes

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.