Data Science Accelerated Master's Programs

Earn Your Master's Degree Faster

If you're planning to pursue an advanced degree after your undergraduate studies, move through your course requirements faster with an accelerated master's degree. Dedicated and high-achieving undergraduate students can double count up to 9 credits toward both a bachelor's and master's degree, as of Michigan Tech's 2026-27 academic year.

Students who are accepted to the accelerated Master of Science in Data Science program are considered to be graduate students upon completion of their bachelor’s degree. All graduate students must adhere to the continuous enrollment policy, along with all other Graduate School and University policies.

"Participating in the accelerated data science master's allows me to increase the depth of my skills and improve my versatility in the job market for only an additional year of study. Data science perfectly blends three of my biggest passions: science, mathematics, and statistics"
Nathaneal Judge, accelerated master's student, data science

Accelerated MS Requirements

Admissions

  • All bachelor’s degree seeking students from Michigan Tech with a cumulative graduating GPA of 3.0 or higher are eligible to enter an accelerated MS program in the Department of Data Science.
  • Students with an overall GPA of 3.0 or higher can apply for admission to an accelerated MS program anytime upon attaining junior class standing, but must apply prior to being awarded their bachelor’s degree.
  • Students already enrolled in a graduate program may not retroactively enroll in an accelerated MS program.
  • The standard Graduate School and program admissions process applies; two letters of reference, a resume, non-Michigan transcripts (if applicable), and student statements are required.
  • Upon acceptance, each student must meet with the appropriate graduate program director to document the specific double-counted courses and allowed Senior Rule courses.

General Accelerated Program Requirements

  • Only students in good academic standing, as defined by the Graduate School, are eligible to enter Michigan Tech's accelerated master's program. Students must maintain a cumulative GPA of 3.0 or above (on a 4.0 scale).
  • All coursework must be completed within five years of a student's admission to the Graduate School and the accelerated master's program.

Data Science Program Requirements

Students interested in an accelerated MS in Data Science should be enrolled in a bachelor's program in computer science, data science, mathematics, management information systems, electrical and computer engineering, or similar.

We recommend a strong background in programming, computer science, and mathematics and/or statistics.

Health Informatics Program Requirements

Students pursuing an accelerated MS in Health Informatics should be enrolled in an undergraduate degree majoring in information technology, cybersecurity, data science, computer science, statistics, management information systems, software engineering, computer engineering, or a related field.

We recommend a strong background in software, computer science, and programming and/or data management.

Senior Rule

Under the Senior Rule, a student may take up to 10 credit hours of courses toward an accelerated MS degree while still an undergraduate. Senior Rule credits are counted independently of double-counted credits. Senior Rule credits do not count toward your bachelor's degree.

All courses counted under the Senior Rule and all double-counted courses applied to the accelerated MS degree must have a grade of B or higher. See below for a list of courses that can be double counted or used under the Senior Rule.

Advising

  • Students who intend to pursue the master's thesis or report option should meet with a faculty research advisor when they apply to the accelerated master's program. This ensures that scheduled courses match up with degree requirements.
  • We advise that coursework option students complete at least 126 credits before officially beginning graduate study.
  • Upon acceptance, students will work with an assigned department faculty advisor who will supervise research, help students develop an academic plan to enroll in the correct courses, and guide academic and professional growth.
  • Once students have developed an academic plan with their advisor, that plan will need written approval from the faculty advisor and the departmental graduate coordinator.

Courses Eligible for Double Counting and Senior Rule

Both the MS in Data Science and MS in Health Informatics programs offer multiple options for completion. Course requirements will vary based on how you decide to complete your degree.

Check with an advisor for the latest information on courses from each degree option that are eligible for double-counting and the Senior Rule.

Please note, the courses listed here are updated yearly by the program. Consult with the MS in Data Science graduate director for any courses not seen here.

Data Science MS

  • BA5200—Information Systems Management and Data Analytics

  • BA5300—Financial Reporting and Control

  • BA5610—Operations Management

  • BA5600—Project Management

  • BA5800—Marketing, Technology, and Globalization

  • BE5550—Biostatistics for Health Science Research

  • CH4610—Introduction to Polymer Science

  • CH5410—Advanced Organic Chemistry: Reaction Mechanisms

  • CH5420—Advanced Organic Chemistry: Synthesis

  • CH5509—Transport and Transformation of Organic Pollutants

  • CH5515—Atmospheric Chemistry

  • CH5516—Aerosol and Cloud Chemistry

  • CH5560—Computational Chemistry

  • CS3425—Database

  • CS4425—Data Management System Design

  • CS4471—Computer Security

  • CS4811—Artificial Intelligence

  • CS5321—Advanced Algorithms

  • CS5331—Parallel Algorithm

  • CS5441—Distributed System

  • CS5471—Advanced Topics in Computer Security

  • CS5496/EE5496—GPU and Multi-core Programming

  • CS5631—Data Visualizations

  • CS5760—HCI Usability Testing

  • CS5811—Advanced Artificial Intelligence

  • CS5821/EE5821—Computational Intelligence

  • CS5831—Advanced Data Mining

  • CS5841/EE5841—Machine Learning

  • EC4200—Econometrics

  • EC4400—Banking and Financial Institutions

  • EE5496/CS5496—GPU and Multi-core Programming

  • EE5500—Probability and Stochastic Processes

  • EE5521—Detection & Estimation Theory

  • EE5726—Wireless Sensor Networks

  • EE5821/CS5821—Computational Intelligence

  • EE5841/CS5841—Machine Learning

  • FIN3000—Principles of Finance

  • FIN4200—Derivatives and Financial Engineering

  • FW3540—Introduction to Geographic Information Systems for Natural Resource Management

  • FW5083—Programming Skills for Bioinformatics

  • FW5084—Data Presentation and Visualization with R

  • FW5411—Applied Regression Analysis

  • FW5412—Regression with the R

  • FW5540—Remote Sensing of the Environment

  • FW5550—Geographic Information Systems and Spatial Analysis

  • FW5555—Advanced GIS Concepts and Analysis

  • FW5556—GIS Project Management

  • FW5560—Digital Image Processing: A Remote Sensing Perspective

  • GE5150—Advanced Natural Hazards

  • GE5195—Volcano Seismology

  • GE5250—Advanced Computational Geosciences

  • GE5600—Advanced Reflection Seismology

  • GE5870—Geostatistics & Data Analysis

  • MA3710—Engineering Statistics

  • MA3715—Biostatistics

  • MA3740—Statistical Programming and Analysis

  • MA4330—Linear Algebra

  • MA4710—Regression Analysis

  • MA4720—Design and Analysis of Experiments

  • MA5201—Combinatorial Algorithms

  • MA5221—Graph Theory

  • MA5627—Numerical Linear Algebra

  • MA5630—Numerical Optimization

  • MA5701—Statistical Methods

  • MA5741—Multivariate Statistical Methods

  • MA5750—Statistical Genetics

  • MA5761—Computational Statistics

  • MA5770—Bayesian Statistics

  • MA5781—Time Series Analysis and Forecasting

  • MA5790—Predictive Modeling

  • MA5791—Categorical Data Analysis

  • MIS3100—Business Database Management

  • MIS3200—Systems Analysis and Design

  • MIS3400—Business Intelligence

  • MIS4990—Special Topics in Management Information Systems

  • MGT4600—Management of Technology and Innovation

  • MKT3200—Consumer Behavior

  • MKT3600—Marketing Data Analytics

  • PH4390—Computational Methods in Physics

  • PSY5210—Advanced Statistical Analysis and Design I

  • PSY5220—Advanced Statistical Analysis and Design II

  • SAT3002—Application Programming Introduction

  • SAT3210—Database Management

  • SAT3611—Infrastructure Service Administration and Security

  • SAT5001—Introduction to Medical Informatics

  • SAT5114—Introduction to Artificial Intelligence in Health

  • SAT5141—Clinical Support Modeling

  • SAT5151—Application Integration and Interoperability

  • SAT5241—Designing Security Systems

  • SAT5283—Information Governance and Risk Management

  • SAT5424—Population Health Management and Monitoring

  • SAT5761—Introduction to Hadoop and Applications

  • SS5005—Introduction to Computational Social Science

  • SS5315—Population and Environment

  • SU5010—Geospatial Concepts, Technologies, and Data

  • UN5000—Graduate Cooperative Education I

  • UN5390—Scientific Computing

  • UN5550—Introduction to Data Science