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MICHIGAN STATE UNIVERSITY
College of Natural Science
Master of Science degree program in Data Science

Prospective Students

The MS in Data Science Program is currently accepting applications for Fall 2026.  The priority deadline for applications is January 15, 2026.  All applications made before January 15 will be considered.  Applications after January 15 will be considered on a rolling basis.

Information sessions for prospective students for the Fall 2026 MSDS program will be held on the following dates:

Academic Profiles  |  Program Graduates  |  Courses  |  Tuition  |  Apply Now

Student Academic Profiles

The MS in Data Science program is recruiting students with strong undergraduate backgrounds who have strong curiosity and technical aptitude. Successful students have come from a wide variety of different backgrounds.  Some of our students are recent graduates, while others have been working in their careers for a substantial amount of time.  Here are two common student profiles:

  • Data Science for X.  This student comes from a specific domain or industry and possesses in-depth knowledge and experience in that field. They recognize the value of data-driven decision-making in their industry and are motivated to learn data science to leverage the power of data for improved insights and outcomes. They may have backgrounds in fields such as healthcare, supply chain, marketing, or social sciences, and are eager to combine their domain expertise with data science techniques to drive innovation.

  • Tech Enthusiast. This student is characterized by a strong background in a quantitative field and/or coding. They may have experience in programming, mathematics, sciences, or statistics. Their passion for technology and data-driven solutions drives them to pursue a master's in data science to deepen their knowledge and gain expertise in the field. They are likely to excel in coding and/or technical problem-solving.  They also have a strong curiosity to look deeply into a problem and formulate a question that data science can answer.

Some Calculus and programming background (no specific language) is required.  Some introduction to probability and statistics is strongly recommended.  Multivariable calculus and Linear algebra are not required, but they are greatly helpful in the data science education.

Program Graduates

With their computational and analytical skills, the program's graduates can:

  • Assimilate, process, and in​terpret data from rich and diverse sources, or from large and potentially distributed data sets.
  • Build computational, mathematical and statistical models which infer meaningful relationships in data and can be used for interpretation and predictive analytics.
  • Create visualizations to aid in the understanding of their data and models.
  • Communicate their findings and insights to a variety of audiences so that decisions can be made, and action can be taken.

Courses

The MSDS is a 30-credit graduate degree, comprised of 18 required credits, 9 elective credits, and a 3-credit capstone course. Please visit the MSU Registrar course search page for MSU catalog course descriptions.

Six required courses (18 credits) for this program are balanced between the three units:

  • STT 810, a course on probability and mathematical statistics for data scientists at MS level
  • STT 811, a course on applied statistical methodology for data scientists at MS level
  • CSE 482, a computer-science course on big data analysis which includes collecting, storing, preprocessing and analyzing large amounts of data.
  • CSE 881, a computer-science course on data mining, at MS level.
  • CMSE 830, a foundational course on algorithms and methods in Data Science at MS level
  • CMSE 831, a foundational course on applied and computational optimization for data scientists, including implementation, at MS level.

9 credits of elective courses draw from a broad set of courses in the three units. Students with the 6 required courses above are well-prepared for taking electives. The list of electives includes the following, and may include other courses approved by the MS DS committee:

  • STT 802, statistical computation using the specialized software R.
  • STT 812, a compact course on modern statistical data analysis, including statistical learning
  • STT 844, a course on time series analysis
  • STT 873, a course on statistical learning and data mining
  • STT 874, a course on Bayesian analysis
  • STT 875, a course on R programming for statistics
  • CSE 802, a course on pattern recognition
  • CSE 830, a course on the design and analysis of algorithms
  • CSE 840, a course on computation foundations of AI
  • CSE 847, a course on machine learning
  • CSE 849, a course on deep learning
  • CMSE/CSE 822, a joint course on parallel computing
  • CMSE 402, a course on communication in data science.
  • Other CMSE elective courses which are being developed at MSU, some of which are topics courses which have already been taught in CSME, and could be taught jointly with other units. Plans exist for the following topics:
    • CMSE 890 Uncertainty Quantification (has been taught)
    • CMSE 890 Applied Topology (has been taught)
    • CMSE 890 Probabilistic Graphical Models (planned)
    • CMSE 890 Mathematical Image Processing (planned)
    • CMSE 890 Biomedical Science Data (planned)
    • CMSE 890 Applied Machine Learning for Biomedicine (planned)
    • CMSE 890 Computational Methods for Machine Learning (planned)
  • Other statistics topics courses STT 890 approved by the MS DS committee.
  • Other computer science topics courses CSE 890 approved by the MS DS committee. 
  • Any graduate-level MSU course covering data science topics which can be approved by the MS DS committee.

A 3-credit capstone course involves completion of an applied, industrial, or governmental data-science project. Credit for this course can be recorded as one of the three topics courses:

  • STT 890
  • CSE 890
  • CMSE 890

The program is building a portfolio of case studies by featuring capstone projects driven by industry, government, or academia clients.

Tuition

The MSDS applies a flat fee of $39,000 for in-state, out-of-state, and international students for the program.  This fee will typically be paid on a semester-by-semester basis, so that each semester a student will be charged $9,750, for 1-9 credits.  Other payment options will be available for self-supported residents of Michigan, and for students wishing to complete the program in more than 2 years.Please contact MSDS.Info@msu.edu for tuition questions.