Materials Science and Engineering B.Mat.S.E.

In response to industry needs, the Department of Chemical Engineering and Materials Science (CEMS) developed a coursework-based M.S. in Data Science for Chemical Engineering and Materials Science (M.S. DS-CEMS) degree program emphasizing statistics, computing, data analysis, and optimization and their application in chemical engineering and materials science. This M.S. program was developed in coordination with Computer Science and Statistics. The integrated B.MatS.E/M.S.DS-CEMS program will give students in the undergraduate Materials Science & Engineering program the option to apply to the DS-CEMS master’s program and obtain a bachelor’s and master’s degree within five years, providing a time and cost benefit to the students while earning their professional degrees.

Application Requirements:

Applicants must be enrolled in the B.MatS.E program at the University of Minnesota-Twin Cities and in their junior (third) year of the Materials Science & Engineering program. Applicants must have a minimum cumulative GPA of at least 3.0 at the time of application, or a strong recommendation from a CEMS faculty member or instructor.

In addition to completing all of the requirements for the B.MatS.E degree, students must also complete the following courses with a C- or better prior to the beginning of graduate coursework:

Programming requirement (one of the following)

CSci 1103: Introduction to Computer Programming in Java
CSci 1113: Introduction to C/C++ Programming for Scientists and Engineers
CSci 1133: Introduction to Computing and Programming (recommended)

Algorithms requirement

CSci 3041: Introduction to Discrete Structures and Algorithms

Statistics requirement (one of the following)

Stat 3021: Introduction to Probability and Statistics
Stat 3301: Regression and Statistical Computing

Students apply to an integrated degree program the semester prior to the last year of undergraduate academic study, providing the student one year (two semesters) to complete undergraduate degree requirements and also take graduate-level courses. Admission can be revoked should the student not successfully complete all the admission requirements.

Students will apply through the Graduate School online application system, including:

application fee,
transcript,
statement of purpose,
and two letters of recommendation with one being from a faculty member in CEMS.

Students should also note if they plan on doing a project-based MS or a course-based MS (Plan B or Plan C).

Students can transfer a maximum of 12 credits taken during their integrated senior undergraduate year to the graduate program. Students will complete a minimum of one year/18 credits as a graduate student before completing master’s program requirements. Coursework applied to the graduate degree must be taken at the graduate level (i.e., 5xxx or above). Credits cannot be applied to the undergraduate degree (i.e., no "double dipping").

Students admitted to this integrated degree program will complete and be awarded the undergraduate degree within 4 years (8 semesters) for NHS and 3 years (6 semesters) for NAS students. Admission will be revoked if the awarding of the undergraduate degree exceeds 8 semesters for NHS students and 6 semesters for NAS students.

The bachelor’s and master’s degrees cannot be awarded simultaneously.

Credits cannot be (double counted) used for both the undergraduate and graduate degree requirements. This includes credits used for other undergraduate degrees, majors, or minors.

Credits used for undergraduate degrees, majors, or minors cannot be split between the undergraduate and graduate degree programs.

Courses that will be used to fulfill the master’s degree requirements must appear in the undergraduate degree sub-plan by the tenth day of the semester in which the student is enrolled in the courses. Any final edits or updates to this sub-plan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded.  Courses not in this sub-plan by that time cannot be updated later and, therefore, will not be eligible for use towards the master’s degree.

Students should consider taking the following courses/requirements to apply toward their graduate degree as an undergraduate integrated program student (12 credits max):

• ChEn 5751, Biochemical Engineering

• ChEn 5753, Advanced Biomedical Transport Processes

• ChEn/MatS 5801, Optimization in Chemical and Energy Systems Engineering

• ChEn/MatS 5802, Applied Machine Learning in Chemical Engineering and Materials Science

• ChEn/MatS 5803, Chemical and Materials Technology Commercialization

• ChEn/MatS 8001, Structure and Symmetry of Materials

• ChEn 8101, Fluid Mechanics

• ChEn 8102, Introduction to Rheology

• ChEn 8104, Coating Process Fundamentals

• ChEn/MatS 8201, Applied Math

• ChEn/MatS 8221, Synthetic Polymer Chemistry

• ChEn/MatS 8301, Physical Rate Processes 1: Transport

• ChEn 8401, Physical and Chemical Thermodynamics

• ChEn 8402, STatistical Thermodynamics and Kinetics

• ChEn 8501, Chemical Rate Processes: Analysis of Chemical Reactors

• ChEn 8754, Systems Analysis of Biological Processes

• MatS 5517, Microscopy of Materials

• MatS 8002, Electronic Properties

• MatS 8004, Mechanical Properties

• MatS 8217, Transmission Electron Microscopy

• CSci 5521, Machine Learning Fundamentals

• CSci 5523, Introduction to Data Mining

• CSci 5525, Machine Learning 

• CSci 5103, Operating Systems (3 credits)

• CSci 5105, Introduction to Distributed Systems (3 credits)

• CSci 5211, Data Communications and Computer Networks (3 credits)

• CSci 5271, Introduction to Computer Security (3 credits)

• CSci 5302, Analysis of Numerical Algorithms (3 credits)

• CSci 5304, Computational Aspects of Matrix Theory (3 credits)

• CSci 5451, Introduction to Parallel Computing: Architectures, Algorithms and Programming (3 credits)

• CSCI 5511 - Artificial Intelligence I (3.0 cr)

• CSCI 5512 - Artificial Intelligence II (3.0 cr)

• CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)

• CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)

• CSCI 5609 - Visualization (3.0 cr)

• CSCI 5707 - Principles of Database Systems (3.0 cr)

• CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)

• CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)

• CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)

• CSCI 5801 - Software Engineering I (3.0 cr)

• CSCI 5802 - Software Engineering II (3.0 cr)

• CSCI 8102 - Foundations of Distributed Computing (3.0 cr)

• CSCI 8314 - Sparse Matrix Computations (3.0 cr)

• CSCI 8581 - Big Data in Astrophysics (4.0 cr)

• CSCI 8701 - Overview of Database Research (3.0 cr)

• CSCI 8715 - Spatial Data Science Research (3.0 cr)

• CSCI 8725 - Databases for Bioinformatics (3.0 cr)

• CSCI 8735 - Advanced Database Systems (3.0 cr)

• CSCI 8801 - Advanced Software Engineering (3.0 cr)

• EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)

• EE 5251 - Optimal Filtering and Estimation (3.0 cr)

• EE 5271 - Robot Vision (3.0 cr)

• EE 5351 - Applied Parallel Programming (3.0 cr)

• EE 5355 - Algorithmic Techniques for Scalable Many-core Computing (3.0 cr)

• EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)

• EE 5389 - Introduction to Predictive Learning (3.0 cr)

• EE 5501 - Digital Communication (3.0 cr)

• EE 5531 - Probability and Stochastic Processes (3.0 cr)

• EE 5542 - Adaptive Digital Signal Processing (3.0 cr)

• EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0 cr)

• EE 5571 - Statistical Learning and Inference (3.0 cr)

• EE 5581 - Information Theory and Coding (3.0 cr)

• EE 5585 - Data Compression (3.0 cr)

• EE 8231 - Optimization Theory (3.0 cr)

• EE 8551 - Multirate Signal Processing and Applications (3.0 cr)

• EE 8591, Predictive Learning from Data

• IE 5531 - Engineering Optimization I (4.0 cr)

• IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)

• IE 8521 - Optimization (4.0 cr)

• IE 8531 - Discrete Optimization (4.0 cr)

• MSBA 6321 - Data Management, Databases, and Data Warehousing (3.0 cr)

• MSBA 6331 - Big Data Analytics (3.0 cr)

• PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)

• PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)

• PUBH 7405 - Biostatistical Inference I (4.0 cr)

• PUBH 7406 - Biostatistical Inference II (3.0 cr)

• PUBH 7407 - Analysis of Categorical Data (3.0 cr)

• PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)

• PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)

• PUBH 7460 - Advanced Statistical Computing (3.0 cr)

• PubH 7475, Statical Learning and Data Mining

• PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)

• PUBH 7485 - Methods for Causal Inference (3.0 cr)

• PUBH 8401 - Linear Models (3.0 cr)

• PUBH 8432 - Probability Models for Biostatistics (3.0 cr)

• PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)

• PubH 8475/Stat 8056, Statistical Learning and Data Mining

• STAT 5052 - Statistical and Machine Learning (3.0 cr)

• STAT 5102 - Theory of Statistics II (4.0 cr)

• STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)

• Stat 5302, Applied Regression Analysis

• STAT 5303 - Designing Experiments (4.0 cr)

• STAT 5401 - Applied Multivariate Methods (3.0 cr)

• STAT 5421 - Analysis of Categorical Data (3.0 cr)

• STAT 5511 - Time Series Analysis (3.0 cr)

• STAT 5601 - Nonparametric Methods (3.0 cr)

• STAT 5701 - Statistical Computing (3.0 cr)

• STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)

• STAT 8101 - Theory of Statistics 1 (3.0 cr)

• STAT 8102 - Theory of Statistics 2 (3.0 cr)

• STAT 8112 - Mathematical Statistics II (3.0 cr)

• AST/Stat 5731 - Bayesian Astrostatistics (4.0 cr)

• CSCI 8205/EE 8367 - Parallel Computer Organization (3.0 cr)

• MATH 5651/Stat 5101 - Basic Theory of Probability and Statistics (4.0 cr)

• PUBH 8475/Stat 8056 - Statistical Learning and Data Mining (3.0 cr)