MS Mathematics: Industrial & Applied Math
Students in the University of Minnesota Master of Science: Mathematics with an emphasis in Industrial and Applied Mathematics program explore the core of mathematical innovation. Our comprehensive curriculum is meticulously crafted to blend theoretical foundations with real-world applications. You'll study advanced mathematical theories, computational techniques, and modeling methods that are directly applicable to industry professions.
Plan type and coursework
The Master of Mathematics with an emphasis in Industrial and Applied Mathematics can be completed as a Plan A, B, or C Master’s. Plan A students take fewer courses, and complete 10 thesis credits and while writing their Master’s thesis. Regular Participation in the Industrial Problems seminar is a requirement of this program. Students entering the Master’s program are required to have completed an undergraduate degree prior to matriculation into the program. The minimum GPA of the Master’s program is 3.0. Coursework used to fulfill the degree requirements must be taken on a grade basis when offered. Students entering the Master’s program are required to have completed an undergraduate degree prior to matriculation into the program.
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Methods of Applied Mathematics Requirement – 2 Courses
Methods of Applied Mathematics Requirement – 2 Courses
All plan types take 6 credits.
- MATH 8401: Mathematical Modeling and Methods of Applied Mathematics
- MATH 8402: Mathematical Modeling and Methods of Applied Mathematics
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Applied Mathematics Electives – Courses based on your plan
Applied Mathematics Electives – Courses based on your plan
Plan A: Choose 3 courses
Plan B or C: Choose 6 courses
- MATH 5248: Cryptology and Number Theory
- MATH 5251: Error-Correcting Codes, Finite Fields, Algebraic Curves
- MATH 5385: Introduction to Computational Algebraic Geometry
- MATH 5445: Mathematical Analysis of Biological Networks
- MATH 5467: Introduction to the Mathematics of Image and Data Analysis
- MATH 5486: Introduction To Numerical Methods II
- MATH 5490: Topics in Applied Mathematics
- MATH 5465: Mathematics of Machine Learning and Data Analysis
- MATH 5651: Basic Theory of Probability and Statistics
- MATH 5654: Prediction and Filtering
- MATH 5711: Linear Programming and Combinatorial Optimization
- MATH 8172: Model Theory
- MATH 8173: Model Theory
- MATH 8280: Topics in Number Theory
- MATH 8387: Mathematical Modeling of Industrial Problems
- MATH 8388: Mathematical Modeling of Industrial Problems
- MATH 8442: Numerical Analysis and Scientific Computing
- MATH 8441: Numerical Analysis and Scientific Computing
- MATH 8540: Topics in Mathematical Biology
- MATH 8581: Applications of Linear Operator Theory
- MATH 8582: Applications of Linear Operator Theory
- MATH 8600: Topics in Advanced Applied Mathematics
- MATH 8651: Theory of Probability Including Measure Theory
- MATH 8652: Theory of Probability Including Measure Theory
- MATH 8654: Fundamentals of Probability Theory and Stochastic Processes
- MATH 8991: Independent Study
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Outside Coursework – 6 Courses
Outside Coursework – 6 Courses
Students may choose from the below courses or complete a Master’s level minor in Data Science, Computer Science, Statistics or BioStatistics. Courses in Electrical and Computer Engineering or Industrial and Systems Engineering may also be used to meet this requirement in consultation with the advisor.
- CSCI 5103:Operating Systems
- CSCI 5105: Introduction to Distributed Systems
- CSCI 5106: Programming Languages
- CSCI 5115: User Interface Design, Implementation and Evaluation
- CSCI 5117: Developing the Interactive Web
- CSCI 5123: Recommender Systems
- CSCI 5125: Collaborative and Social Computing
- CSCI 5127W: Embodied Computing: Design & Prototyping
- CSCI 5143: Real-Time and Embedded Systems
- CSCI 5161: Introduction to Compilers
- CSCI 5204: Advanced Computer Architecture
- CSCI 5211: Data Communications and Computer Networks
- CSCI 5271: Introduction to Computer Security
- CSCI 5302: Analysis of Numerical Algorithms
- CSCI 5304: Computational Aspects of Matrix Theory
- CSCI 5421: Advanced Algorithms and Data Structures
- CSCI 5451: Introduction to Parallel Computing: Architectures, Algorithms, and Programming
- CSCI 5461: Functional Genomics, Systems Biology, and Bioinformatics
- CSCI 5471: Modern Cryptography
- CSCI 5481: Computational Techniques for Genomics
- CSCI 5511: Artificial Intelligence I
- CSCI 5512: Artificial Intelligence II
- CSCI 5521: Machine Learning Fundamentals
- CSCI 5523 : Introduction to Data Mining
- CSCI 5525: Machine Learning: Analysis and Methods
- CSCI 5527: Deep Learning: Models, Computation, and Applications
- CSCI 5541: Natural Language Processing
- CSCI 5551: Introduction to Intelligent Robotic Systems
- CSCI 5552: Sensing and Estimation in Robotics
- CSCI 5561: Computer Vision
- CSCI 5563: Multiview 3D Geometry in Computer Vision
- CSCI 5607: Fundamentals of Computer Graphics 1
- CSCI 5608: Fundamentals of Computer Graphics II
- CSCI 5609: Visualization
- CSCI 5611: Animation & Planning in Games
- CSCI 5619: Virtual Reality and 3D Interaction
- CSCI 5707: Principles of Database Systems
- CSCI 5708: Architecture and Implementation of Database Management Systems
- CSCI 5715: From GPS, Google Maps, and Uber to Spatial Data Science
- CSCI 5751: Big Data Engineering and Architecture
- CSCI 5801: Software Engineering I
- CSCI 5802: Software Engineering II
- CSCI 5980: Special Topics in Computer Science
- CSCI 8101: Advanced Operating Systems
- CSCI 8102: Foundations of Distributed Computing
- CSCI 8115: Human-Computer Interaction and User Interface Technology
- CSCI 8117: Understanding the Social Web
- CSCI 8161: Advanced Compiler Techniques
- CSCI 8205: Parallel Computer Organization
- CSCI 8211: Advanced Computer Networks and Their Applications
- CSCI 8271: Security and Privacy in Computing
- CSCI 8314: Sparse Matrix Computations
- CSCI 8363: Numerical Linear Algebra in Data Exploration
- CSCI 8442: Computational Geometry and Applications
- CSCI 8551: Intelligent Agents
- CSCI 8581: Big Data in Astrophysics
- CSCI 8701: Overview of Database Research
- CSCI 8715: Spatial Data Science Research
- CSCI 8725: Databases for Bioinformatics
- CSCI 8735: Advanced Database Systems
- CSCI 8801: Advanced Software Engineering
- CSCI 8980: Special Advanced Topics in Computer Science
- EE 8591: Predictive Learning from Data
- PUBH 7401: Fundamentals of Biostatistical Inference
- PUBH 7402: Biostatistics Modeling and Methods
- PUBH 7415: Introduction to Clinical Trials
- PUBH 7420: Clinical Trials: Design, Implementation, and Analysis
- PUBH 7430: Statistical Methods for Correlated Data
- PUBH 7440: Introduction to Bayesian Analysis
- PUBH 7445: Statistics for Human Genetics and Molecular Biology
- PUBH 7450: Survival Analysis
- PUBH 7470: Study Designs in Biomedical Research
- PUBH 7475: Statistical Learning and Data Mining
- PUBH 7485: Methods for Causal Inference
- PUBH 8475: Statistical Learning and Data Mining
- STAT 4051: Statistical Machine Learning I
- STAT 4052: Statistical Machine Learning II
- STAT 4101: Theory of Statistics I
- STAT 4102: Theory of Statistics II
- STAT 5021: Statistical Analysis
- STAT 5101: Theory of Statistics I
- STAT 5102: Theory of Statistics II
- STAT 5201: Sampling Methodology in Finite Populations
- STAT 5302: Applied Regression Analysis
- STAT 5303: Designing Experiments
- STAT 5401: Applied Multivariate Methods
- STAT 5421: Analysis of Categorical Data
- STAT 5511: Time Series Analysis
- STAT 5601: Nonparametric Methods
- STAT 5701: Statistical Computing
- STAT 5931: Topics in Statistics
- STAT 8051: Advanced Regression Techniques: linear, nonlinear and nonparametric methods
- STAT 8052: Applied Statistical Methods 2: Design of Experiments and Mixed
- STAT 8053: Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression
- STAT 8054: Statistical Methods 4: Advanced Statistical Computing
- STAT 8056: Statistical Learning and Data Mining
- STAT 8101: Theory of Statistics 1
- STAT 8102: Theory of Statistics 2
- STAT 8111: Mathematical Statistics I
- STAT 8112: Mathematical Statistics II
- STAT 8311: Linear Models
- STAT 8312: Linear and Nonlinear Regression
- STAT 8321: Regression Graphics
- STAT 8401: Topics in Multivariate Methods
- STAT 8411: Multivariate Analysis
- STAT 8421: Theory of Categorical Data Analysis
- STAT 8501: Introduction to Stochastic Processes with Applications
- STAT 8511: Time Series Analysis
- STAT 8931: Advanced Topics in Statistics
- STAT 8932: Advanced Topics in Statistic
Tuition and funding
Offers of admission to our MS Mathematics: Industrial and Applied Mathematics program do not come with an offer of funding. Students can find a number of financial aid opportunities through the Funding page of the Graduate School’s website.