MS – Data Engineering

What is the UW-Madison MS-Data Engineering Degree?

Data engineers ensure that increasing amounts of data being generated and processed on a daily basis are usable by the time they reach data scientists and analysts. This program prepares students for employment in this exponentially growing field.

Who is the program for?

This program is for anyone with a background in computer science who wants to pursue a career in data engineering. If you're interested in making high-quality data accessible to scientists and analysts, this degree is for you.

Questions? Need more information? Email us!

Current students: See specific program requirements in the Guide.

Admissions

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Dates and to apply

Fall admissions only:

  • 2022 deadline: July 15
  • 2023 deadline: March 15

To apply: UW-Madison Graduate School Application Process

Application materials and requirements

Applicants to the program should have completed a bachelor’s degree in computer science or a related field.

GRE not required.

Three letters of recommendation required.

English proficiency if your native language is not English: See requirements on the Graduate School Application Page.

Curricular Requirements

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.

Sample Curriculum: 30 credits required

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Data Engineering Foundations - complete all classes

COMP SCI 739
Distributed Systems

COMP SCI 744
Big Data Systems

COMP SCI 764
Topics in Database Management Systems

COMP SCI 838
Topics in Computing 1

Machine Learning - select a minimum of two courses

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 - select a minimum of 1 course

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 - select a minimum of one course

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: select a minimum of one course

COMP SCI 765
Data Visualization

COMP SCI/ED PSYCH/PSYCH 770
Human-Computer Interaction

Approved Electives: select any course from above or from the list below

COMP SCI 642
Introduction to Information Security

COMP SCI 702
Graduate Cooperative Education 2

COMP SCI 790
Master’s Thesis 2

COMP SCI 799
Master’s Research 2

COMP SCI 900
Advanced Seminar in Computer Science 2

STAT 611
Statistical Models for Data Science

STAT 612
Statistical Inference for Data Science

STAT 613
Statistical Methods for Data Science

MS-Data Engineering Faculty & Staff

Remzi Arpaci-Dusseau

Department Chair

Suman Banerjee

Head of Professional Programs

Professor

Janna Boehm

PMP/PCP/MSDE, Computer Sciences

Professional Programs Manager

AnHai Doan

Professor

Cindy Fendrick

Department Administrator for Academic Services

Yong Jae Lee

Associate Professor

Paris Koutris

Assistant Professor

Shivaram Venkataraman

Assistant Professor

Xiangyao Yu

Assistant Professor