Data Science MS Degree Program

Data Science MS Degree Program
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Computer Science
Michigan Tech
Computing
Computer Science
Graduate
Data Science—MS
Data Science MS Degree Program
The Michigan Tech Advantage
The Michigan Tech Data Science MS provides a broad-based education in data mining,
predictive analytics, cloud computing, data-science fundamentals, communication, and
business acumen. You'll gain a competitive edge through domain-specific specialization
in disciplines of science and engineering, and you'll have the freedom to explore
and develop your own interests in one or more domains.
Data Science is a respected, well-paid career.
Data Science is a rapidly growing field. Accordingly, the
U.S. Bureau of Labor Statistics
forecasts
34%
growth
in employment for data scientists from 2024 to 2034, much faster than average. The
mean entry-level salary is
$87,943
(Payscale) with a mean annual wage of $124,590 (BLS) and the top 10 percent making
$194,410 (BLS).
See additional
computing salary information
Figures from payscale.com, accessed April 2025.
Figures from
U.S. Bureau of Labor Statistics
(BLS), dated May 2024.
Program Prerequisites
Entry into the Data Science MS program assumes basic knowledge in statistical and
mathematical techniques, computer programming, information systems and databases,
and communications, obtained through a degree in business, math, computing, science,
or an engineering discipline.
The best parts of Computing[MTU] are the quality of the coursework and the helpful
nature of the professors.
Navjot Kaur
Graduate Student, Data Science MS
Data Science Career Opportunities
Data Scientist
Data Analyst
Data Engineer
Data Architect
Machine Learning Scientist
Machine Learning Engineer
Business Intelligence Developer
Data Storyteller
Database Administrator
See additional
computing salary information
Figures from payscale.com, accessed April 2025.
Figures from
U.S. Bureau of Labor Statistics
(BLS), dated May 2024.
Michigan Tech Graduates Land Jobs at Major Employers
Adobe Systems
AFLAC
AMD
Amazon.com
Apple
Battle Creek Public Schools
Capital One
Fannie Mae
Ford Motor
GE Aviation
Google
IBM Corporation
Intel
Lucent Technologies
Microsoft
Oracle
Pfizer Inc.
Texas Instruments
US Department of Defense
No. 1
best colleges in Michigan with no application fee
No. 2
best return on investment (ROI) of public colleges in Michigan
No. 3
best colleges in Michigan
No. 4
top 20 best public schools for internships
MS Degree Completion Options
MS, Data Science: Thesis Option
This option requires a research thesis prepared under the supervision of the advisor.
The thesis describes a research investigation and its results. The scope of the research
topic for the thesis should be defined in such a way that a full-time student could
complete the requirements for a master’s degree in 12 months or three semesters—following
the completion of coursework—by regularly scheduling graduate research credits.
The minimum requirements are as follows:
Total Credit Requirements
Option Parts
Credits
Coursework (minimum)
20 Credits
Thesis research
6-10 Credits
Total (minimum)
30 Credits
Distribution of Coursework Credit
Distribution
Credits
5000-6000 series (minimum)
18 Credits
3000-4000 (maximum)
6 Credits
Please note: This option is only available for students starting during or after the
26-27 Academic Year.
MS, Data Science: Report Option
This option requires a report describing the results of an independent study project.
The scope of the research topic should be defined in such a way that a full-time student
could complete the requirements for a master’s degree in 12 months or three semesters—following
the completion of coursework—by regularly scheduling graduate research credits.
Of the minimum total of 30 credits, at least 24 must be earned in coursework other
than the project:
Total Credit Requirements
Option Parts
Credits
Coursework (minimum)
24 Credits
Report
2-6 Credits
Total (minimum)
30 Credits
Distribution of Coursework Credit
Distribution
Credits
5000-6000 series (minimum)
18 Credits
3000-4000 (maximum)
10 Credits
Please note: This option is only available for students starting during or after the
26-27 Academic Year.
MS, Data Science: Coursework Option
This option requires a minimum of 30 credits be earned through coursework. A limited
number of research credits may be used with the approval of the advisor, department,
and Graduate School. See
degree requirements
for more information.
A graduate program may require an oral or written examination before conferring the
degree and may require more than the minimum credits listed here:
Distribution of Coursework Credit
Distribution
Credits
5000-6000 series (minimum)
18 Credits
3000-4000 (maximum)
12 Credits
Accelerated MS, Data Science
Bachelor's + 1 Year = Master's Degree
Our accelerated master's degree program
is a faster, easier way for Michigan Tech students to earn a master's degree. Up
to nine approved credits from your bachelor's degree can be applied towards your accelerated
master's degree. Consult your graduate program director for your individualized plan.
If you're thinking about pursuing a master's following your bachelor's this option
may be the right choice for you.
Additional Accelerated Master's Program Details
Request Information
How to Apply
What is Data Science?
Computing Salaries Comparison
Contact
Graduate Program Director:
Laura Brown
Graduate Program Assistant:
Sherry Wyeth
Submit Graduate Student Forms
Additional Resources
Handbook
Advising
Academic Audit Access
Academic Audit Guide
Degree Progress Checklist
Submit Forms
Academic Overview
Degree Completion Timeline
Forms and Deadlines
Graduate Faculty Locator
Graduate Course Descriptions
Graduation and Certification
Policies and Procedures
Professional Resources
Registration (Includes Class Schedule)
Reports
Theses and Dissertations
Course Information
Graduate Course Descriptions
Students in the Data Science program complete courses from four categories: Core Courses,
Elective Courses, Foundational Courses, and Domain Specific/Elective courses.
The coursework option requires a minimum of 30 credits be earned through coursework.
A limited number of research credits may be used for the coursework option with the
approval of the advisor, department, and Graduate School. See
degree requirements
for more information.
These requirements are informational for new students entering the program. Please
refer to your
Academic Audit
or advisor for your specific requirements.
Core Courses, 12 Credits
BA 5200 - Information Systems Management and Data Analytics
Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Accounting, Business Administration, Data Science, Applied Natural Resource Econ., Accounting Analytics, Forensic Accounting, Health Informatics, Accounting and Analytics, Engineering Management
MA 5790 - Predictive Modeling
Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Pre-Requisite(s):
MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)
UN 5550 - Introduction to Data Science
Introduces concepts and skills fundamental to Data Science including: getting data, data wrangling, exploratory data analysis, basic statistics, data visualization, data modeling, and learning. The course introduces data science from different perspectives: computer science, mathematics, business, engineering, and more.
Credits:
3.0
Lec-Rec-Lab:
(2-0-2)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Data Science
Students may choose only
one
of the following to fulfill a core course requirement:
CS 4801 - Foundations of Machine Learning
The course covers classical statistical machine learning. Topics include classification (k-nearest neighbors, decision trees, random forests, logistic regression, naive bayes, neural networks), regression, clustering (partitional and hierarchical clustering), and evaluation.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
DATA 2201 and (MA 2710 or MA 2720 or MA 3710 or MA 3720)
CS 5841 - Deep Learning
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
CS 4801
EE 5841 - Machine Learning
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Research Courses
Taken by students choosing the report (2-6 credits) or thesis (6-10 credits) options.
CS 5990 - Master's Research in Computer Science
The study of an acceptable computer science problem and the preparation of a thesis
Credits:
variable to 9.0;
May be repeated;
Graded Pass/Fail Only
Semesters Offered:
Fall, Spring, Summer
Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
SAT 5999 - Graduate Research in Health Informatics
The study of an acceptable medical informatics research problem and the preparation of a thesis or report.
Credits:
variable to 10.0;
Repeatable to a Max of 10;
Graded Pass/Fail Only
Semesters Offered:
On Demand
Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
Foundational Courses, Maximum of 6 Credits
A maximum of six credit hours of foundational skills courses at the 3000–4000 level
may be applied to the Master of Science in Data Science. These courses will build
skills necessary for successful completion of the MS in Data Science. Some students
will not need to take these foundational courses and will instead use the domain electives
to reach the credit requirements of this program.
CS 3425 - Introduction to Database Systems
This course provides an introduction to database systems including database design, query, and programming. Topics include goals of database management; data definition; data models; data normalization; data retrieval and manipulation with relational algebra and SQL; data security and integrity; database and Web programming; and languages for representing semi-structured data.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Pre-Requisite(s):
(CS 2311 or MA 3210) and CS 2321
FW 3540 - An Introduction to Geographic Information Systems for Natural Resource Management
The fundamentals of GIS and its application to natural resource management. Spatial data, its uses and limitations are evaluated. Students work extensively with the ARCGIS software package.
Credits:
4.0
Lec-Rec-Lab:
(3-0-3)
Semesters Offered:
Spring
Pre-Requisite(s):
MA 2710(C) or MA 2720(C) or MA 3710(C) or ENVE 3502 or CEE 3502(C)
MA 3740 - Statistical Programming and Analysis
Project-based course enabling students to identify statistical methods and analysis using R. Topics include exploratory data analysis, classical statistical tests, sample size and power considerations, correlation, regression, and design experiments using advanced programming techniques.
Credits:
3.0
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Fall, Spring
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710 or MA 3715
MIS 3100 - Business Database Modeling and Management
Emphasizes database principles that are constant across different database software products through concrete examples using a relational database management system. Provides a well-rounded business perspective about modeling, developing, utilizing, and managing organizational databases.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
MIS 2000 or MIS 2100 or CS 1122 or CS 1131
SAT 3210 - Database Management
Introductory course on database management. Topics include data modeling, database design, implementation techniques, SQL Language, database administration and security.
Credits:
3.0
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Class(es): Junior, Senior
Pre-Requisite(s):
SAT 2711 or (CS 2311 and CS 2321)
SAT 3611 - Infrastructure Service Administration and Security
Administrating Linux and Microsoft servers together to provide infrastructure services to mixed clients. Topics include: DNS; DHCP; file, web, mail, and directory security of these services; and best practices for combining and mixing server platforms in an enterprise environment.
Credits:
3.0
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Fall, Summer
Pre-Requisite(s):
SAT 2711
SAT 4650 - Introduction to Applied Computing in Python Programming
This course introduces students to the Python programming language in applied computing systems and applications. In addition to Python basics, introduction to advanced topics such as file operations, database connection, digital image processing, and artificial intelligence will be discussed, particularly within the field of health informatics.
Credits:
3.0
Lec-Rec-Lab:
(2-0-1)
Semesters Offered:
Fall, Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore
Electives, Minimum of 6 Credits
Take at least one course from two different lists:
Computational
CS 4321 - Introduction to Algorithms
Fundamental topics in algorithm design, analysis, and implementation. Analysis fundamentals include asymptotic notation, analysis of control structures, solving recurrences, and amortized analysis. Design and implementation topics include sorting, searching, and graph algorithms. Design paradigms include greedy algorithms, divide-and-conquer algorithms, and dynamic programming.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s):
(CS 2311 or MA 3210) and CS 2321
CS 4801 - Foundations of Machine Learning
The course covers classical statistical machine learning. Topics include classification (k-nearest neighbors, decision trees, random forests, logistic regression, naive bayes, neural networks), regression, clustering (partitional and hierarchical clustering), and evaluation.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
DATA 2201 and (MA 2710 or MA 2720 or MA 3710 or MA 3720)
CS 5321 - Advanced Algorithms
Design and analysis of advanced algorithms. Topics include algorithms for complex data structures, probabilistic analysis, amortized analysis, approximation algorithms, and NP-completeness. Design and analysis of algorithms for string-matching and computational geometry are also covered.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Pre-Requisite(s):
CS 4321
CS 5821 - Computational Intelligence - Theory and Application
This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
On Demand
Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
CS 5831 - Advanced Data Mining
Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology algorithms (e.g. classification, clustering, association rules) and several applications (e.g. text mining, recommender systems, etc.)
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
DATA 2201 and (MA 2710 or MA 2720 or MA 3710)
CS 5841 - Deep Learning
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
CS 4801
CS 5851 - Advanced Natural Language Processing (NLP)
Students will study computer systems that process and understand human language. We focus on state-of-the-art natural language processing (NLP) algorithms that are trained on data. Students will read, analyze, and present current research in NLP.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
CS 5841
SAT 5114 - Artificial Intelligence in Healthcare
This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI topics such as machine learning, deep learning, risk prediction, medical image analysis, natural language processing of clinical text, computer vision, and the integration of AI, bias in algorithm development, bioethics, and regulation into the clinical environment are covered.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
SAT 4650
SAT 5165 - Introduction to Big Data Analytics
Course will cover concepts and techniques used to analyze big data. We will cover the most important big data processing frameworks (e.g. Hadoop, spark) and GPU techniques. The students will acquire the knowledge of Hadoop architecture, MapReduce, Spark and the capability of programming to analyze big data.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
SAT 4650
EET 4501 - Applied Machine Learning
Introduces the general concepts and algorithms of machine learning (ML) with their implementation and applications to practical problems of modeling, detection, estimation, prediction, and control. Applications include cybersecurity, healthcare, robot vision, remote sensing, automation, and natural language processing.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s):
SAT 4310 or SAT 4650
UN 5390 - Scientific Computing
Set in a Linux environment, students will learn to design computational workflows, translate problems into programs, understand sources of errors, and debug, profile and parallelize the code. Successful completion of FOSS101 and earning its Digital Badge are required prior to registration
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
Business/Information Systems
MGT 4600 - Management of Technology and Innovation
Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring, Summer
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore
BA 5650 - Project Management
Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring, Summer
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100
BA 5740 - Managing Innovation and Technology
An evolutionary strategic perspective is taken viewing how technology strategy evolves from underlying technological competencies, patterns of innovation, sources of external technological knowledge and modes of transfer.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration
MIS 5000 - Emerging Technologies
Focuses on understanding IT for competitive advantage and as an agent of transformation. Topics include managing IT infrastructure and architecture, facilitating information distribution throughout the enterprise, business applications for machine learning and artificial intelligence, and other emerging trends and technologies.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
(MIS 2100 and MIS 3200(C)) or (CS 2321 and CS 3141) or BA 5200 or UN 5550
Mathematics/Statistics
MA 4330 - Linear Algebra
A study of fundamental ideas in linear algebra and its applications. Includes review of basic operations, block computations; eigensystems of normal matrices; canonical forms and factorizations; singular value decompositions, pseudo inverses, least-square applications; matrix exponentials and linear systems of ODEs; quadratic forms, extremal properties, and bilinear forms.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
(MA 2320 or MA 2321 or MA 2330) and MA 3160
MA 4710 - Regression Analysis
Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course. Some prior experience with R is expected.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701
MA 5701 - Statistical Methods
Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring, Summer
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
MA 5741 - Multivariate Statistical Methods
Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
(MA 4710 or MA 4720) and MA 2320
MA 5761 - Computational Statistics
Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
MA 4770(C) or (MA 4700 and MA 5701)
MA 5770 - Bayesian Statistics
The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, in even years
Pre-Requisite(s):
MA 4330 and MA 4710 and MA 4760
MA 5781 - Time Series Analysis and Forecasting
Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
(MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)
MA 5791 - Categorical Data Analysis
Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring, in odd years
FW 5411 - Applied Data Analysis
Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s):
FW 5412
FW 5412 - Data Analysis in R
Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.
Credits:
1.0
Lec-Rec-Lab:
(0-1-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s):
FW 5411
PSY 5210 - Advanced Statistical Analysis and Design I
An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.
Credits:
3.0
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Fall, in even years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Domain Electives, Credits Vary by Degree Option
To complete the Master of Science in Data Science, students must earn the remaining
of the required 30 credits through completion of approved domain-specific Data Science
courses. Students may choose domain-specific courses from one or more domains. Each
student will consult with their advisor in order to determine the appropriate mix
of elective courses and domain-specific courses, given the student’s background, interests,
and career aspirations.
Credit requirements vary by degree option:
Thesis Option
Report Option
Coursework Option
2 to 6 credits
6 to 10 credits
12 credits maximum
Applied Computing
SAT 5001 - Introduction to Health Informatics
Course covers fundamental subjects such as medical decision support systems, telemedicine, medical ethics and biostatistics. Topics include consumer health informatics, international health care systems, global health informatics, and translational research informatics. Students will see medical informatics from a diverse scope of healthcare industry organizations. Scientific writing and communication will be encouraged.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore
SAT 5141 - Clinical Decision Support and AI Modeling
Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. Topics include correlation, differential diagnosis, disease progression, precision medicine, telemedicine, machine learning, deep learning, computer vision, WLP, data preprocessing, AI modeling to improve patient outcomes, and safety.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
SAT 4650
SAT 5283 - Information Governance and Risk Management
Course will consist of the legal and regulatory requirements and security privacy concept principles regarding data management. Best practices of how organizations manage information risk through risk assessment practices and procedures will be conducted.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following College(s): College of Computing, College of Engineering, College of Business
SAT 5424 - Population Health Informatics
This course explores the foundations of population health informatics, including information architecture, data standards and confidentiality. We will examine key concepts related to registries, electronic health records, epidemiological databases, biosurveillance, health promotion, and quality reporting in population health management.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
SAT 5520 - Machine Learning in Security
Study of artificial intelligence and machine learning in cybersecurity. Topics include fundamentals of common machine learning and deep learning algorithms, intelligent threat detection and analysis, user behavior analytics, machine learning in hacking, and automated cybersecurity systems.
Credits:
3.0
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
SAT 3812 and SAT 4310
Biomedical Engineering
BE 5870 - Computer Vision for Microscopic Images
This course teaches how to quantify data out of images, typically from optical microscopes. It covers thresholding, image derivatives, edge-detection, watershed, multi-scale and steerable filters, 3D image processing, feature extraction, PCA, classification, convolutional neural networks, particle tracking, and diffusion analysis.
Credits:
3.0
Lec-Rec-Lab:
(0-1-2)
Semesters Offered:
Fall, in even years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Business and Economics
ACC 5200 - Financial Statement Analysis, Modeling, and Valuation
Study of financial statement analysis and concepts of valuation utilizing accounting based financial information. Methods are applied to encompass decision making, communication, and judgment using problems, cases, and projects.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Accounting Analytics, Accounting, Forensic Accounting, Accounting and Analytics;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
BA 5300 - Financial Reporting and Control
This class covers the collection, reporting, and analysis of financial information with emphasis on the use of that information to support decision making.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Business Administration, Engineering Management, Accounting and Analytics, Accounting
BA 5610 - Operations Management
Applications and case studies focusing on contemporary issues in operations and quality management to include lean manufacturing practices, ERP, quality and environmental management systems/standards, Six Sigma, statistical process control, and other current topics.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration
BA 5650 - Project Management
Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring, Summer
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100
BA 5800 - Marketing, Technology, and Society
The course facilitates students' improvement of analytical skills, information processing techniques, and cultural competence in the globalized marketing environment. Focuses are placed on strategic marketing management, high-tech product marketing, global consumer behavior, branding, and online marketing.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration
EC 4200 - Econometrics
Introduces techniques and procedures to estimate and test economic and financial relationships developed in business, economics, social and physical sciences.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Pre-Requisite(s):
(EC 2001 or EC 3002 or EC 3003) and (BUS 2100 or MA 2710 or MA 2720 or MA 3710) and (MA 1135 or MA 1160 or MA 1161 or MA 1121)
EC 4400 - Banking and Financial Institutions
Analysis of asset and liability management of financial institutions and the role of financial institutions in the U.S. and international economy.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Pre-Requisite(s):
(EC 3003 or FIN 3000)
FIN 3000 - Principles of Finance
Introduction to the principles of finance. Topics include financial mathematics, the capital investment decision, financial assets valuation, and the risk-return relationship
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall, Spring, Summer
Pre-Requisite(s):
ACC 2000 and (MA 1020 or MA 1030 or MA 1031 or MA 1032 or MA 1120 or MA 1135 or MA 1160 or MA 1161 or MA 1121 or MA 2160 or ALEKS Math Placement >= 61 or CEEB Calculus AB >= 2 or CEEB Calculus BC >= 2 or ACT Mathematics >= 22 or SAT MATH SECTION SCORE-M16 >= 540)
FIN 4200 - Derivatives and Financial Engineering
Covers the pricing and use of options, financial futures, swaps, and other derivative securities.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
EC 3400 or FIN 3000 and (MA 2710 or MA 2720 or MA 3710)
MGT 3800 - Innovation & Entrepreneurship
Develops an entrepreneurial mindset and a personal toolkit of methods and practices that enables students to create and evaluate entrepreneurial opportunities, marshal resources, and engage in entrepreneurial teams driven by creativity, leadership, smart action, and innovation.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman
MIS 3200 - Systems Analysis and Design
Provides an understanding of the IS development and modification process and the evaluation choices of a system development methodology. Emphasizes effective communication with users and team members and others associated with the development and maintenance of the information system. Stresses analysis and logical design of departmental-level information system.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
MIS 2100 or CS 1122 or CS 1131
MIS 4400 - Business Intelligence and Analytics
Focuses on generation and interpretation of business analytics relative to organizational decision making. Includes core skills necessary for constructing data retrieval queries in a relational database environment and processing data using appropriate programming languages. Introduces concepts related to data pipelining.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
(MIS 2100 or CS 1122 or CS 1131) and (MIS 3100 or CS 3425)
MIS 4990 - Special Topics in Management Information Systems
Examines current IS/IT topics and issues in greater depth from a managerial perspective. A single offering of this course will concentrate on one or two topics, which will vary.
Credits:
3.0;
Repeatable to a Max of 6
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
On Demand
Pre-Requisite(s):
MIS 2000 or MIS 2100 or CS 1122 or CS 1131
MKT 3200 - Consumer Behavior & Culture
Introduces students to models, theories, practices, and sociocultural issues pertinent to consumers' decision making and lifestyle choices. Discussions will be based on a variety of disciplines: psychology, sociology, economics, and anthropology.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Pre-Requisite(s):
MKT 3000
MKT 3600 - Marketing Data Analytics
Focuses on data-driven consumer insights for marketing decision-making. Topics include scientific research methodology, survey research, social media data-analysis, multivariate data analysis, information visualization, and report writing and presentations.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
(MA 2710 or MA 2720 or MA 3710 or BUS 2100) and MKT 3000
Chemistry
CH 4610 - Introduction to Polymer Science
Introductory study of the properties of polymers. Includes structure and characterization of polymers in the solid state, in solution, and as melts. Topics include viscoelasticity, rubbery elasticity, rheology and polymer processing. Applications discussed include coatings, adhesives, and composites.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Pre-Requisite(s):
CH 1122 or (CH 1160 and CH 1161)
CH 5440 - Molecular Modeling
The course focuses on the principles and applications of the methods for molecular modeling of large molecules. The students will learn the principles of molecular mechanics (MM), molecular dynamics (MD), combined quantum mechanics and molecular mechanics (QM/MM) and their applications for understanding molecular and biomolecular systems.
Credits:
3.0
Lec-Rec-Lab:
(2-0-2)
Semesters Offered:
Fall
Pre-Requisite(s):
CH 3510 and CH 2510
CH 5509 - Transport and Transformation of Organic Pollutants
Assessment of factors controlling environmental fate, distribution, and transformation of organic pollutants. Thermodynamics, equilibrium, and kinetic relationships are used to quantify organic pollutant partitioning and transformations in air, water, and sediments. Use of mass balance equations to quantify pollutant transport.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring, in odd years
Pre-Requisite(s):
CEE 4501 or CH 3510
CH 5515 - Atmospheric Chemistry
Study of the photochemical processes governing the composition of the troposphere and stratosphere, with application to air pollution and climate change. Covers radical chain reaction cycles, heterogeneous chemistry, atmospheric radiative transfer, and measurement techniques for atmospheric gases.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
CH 3510 or ENVE 4501 or ENVE 4504 or CEE 4501 or CEE 4504
CH 5560 - Computational Chemistry
Focuses on the theory and method of modern computational techniques applied to the study of molecular properties and reactivity through lecture and computer projects. Covers classical mechanical as well as quantum mechanical approaches.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
Fall
Pre-Requisite(s):
CH 3520
Computer Sciences
CS 4425 - Database Management System Design
This course covers the design issues concerning the implementation of database management systems, including distributed databases. The topics include data storage, index implementation, query processing and optimization, security, concurrency control, transaction processing, and recovery.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
On Demand
Pre-Requisite(s):
CS 3425
CS 4471 - Computer Security
This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall
Restrictions:
May not be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
CS 3411 or CS 4411
CS 5331 - Parallel Algorithms
Advanced topics in the design, analysis, and performance evaluation of parallel algorithms. Topics include advanced techniques for algorithm analysis, memory models, run time systems, parallel architectures, and program design, particularly emphasizing the interactions of these factors.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
CS 4431 and CS 4331
CS 5341 - Quantum Computing
Quantum Computing (QC) with emphasis on computational aspects of physical quantum systems. Topics include classic vs. quantum computation, introduction to Quantum Mechanics (QM), quantum information, quantum gates and circuits, teleportation, reversible computation, Fourier sampling, Simon's algorithm, Grover's algorithm, Shor's algorithm.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s):
CS 4321
CS 5441 - Distributed Systems
Covers time and order in distributed systems; mutual exclusion, agreement, elections, and atomic transactions; Distributed File Systems, Distributed Shared Memory, Distributed System Security; and issues in programming distributed systems. Uses selected case studies.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Pre-Requisite(s):
CS 4411 and CS 4461
CS 5471 - Computer Security
This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
CS 3411 or CS 4411
CS 5760 - Human-Computer Interactions and Usability Testing
Current issues in human-computer interaction (HCI), evaluation of user interface (UI) design, and usability testing of UI. Course requires documenting UI design evaluation, UI testing, and writing and presenting a HCI survey, concept or topic paper.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
CS 4760
Electrical and Computer Engineering
EE 5500 - Probability and Stochastic Processes
Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall
Restrictions:
Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering, Electrical & Computer Engineer;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
EE 5521 - Detection & Estimation Theory
Detecting and estimating signals in the presence of noise. Optimal receiver design. Applications in communications, signal processing, and radar.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
Pre-Requisite(s):
EE 5500
EE 5522 - Digital Image Processing
Fundamentals of image processing are covered including image representation, geometric transformations, binary image processing, compression, space and frequency domain processing. Computer programming in MATLAB and Python required.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall, Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
EE 5532 - Sensing and Processing for Robotics
Sensing modes, signal and image processing for industrial robotic automation processes. Emphasis placed on widely used sensors, including cameras and 3-D sensors for process control and computer vision for autonomous navigation.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
EE 5522
Forest Resources and Environmental Science
FW 5082 - Gene Expression Data Analysis
This course is designed for students majoring in molecular biology, computer science, data science and related majors to develop fundamental but essential skills for manipulating, preprocessing, and analyzing high throughput gene expression data for pattern extraction and knowledge discovery.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
Fall, in even years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
FW 4099 or CS 1121 or CS 1122 or CS 1131 or CS 1141 or CS 2321
FW 5083 - Programming Skills for Bioinformatics
Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Fall, in odd years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
FW 5411 - Applied Data Analysis
Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s):
FW 5412
FW 5412 - Data Analysis in R
Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.
Credits:
1.0
Lec-Rec-Lab:
(0-1-0)
Semesters Offered:
Spring
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s):
FW 5411
FW 5540 - Remote Sensing of the Environment
Remote sensing principles and concepts. Topics include camera and digital sensor arrays, types of imagery, digital data structures, spectral reflectance curves, applications, and introductory digital image processing.
Credits:
3.0
Lec-Rec-Lab:
(2-1-0)
Semesters Offered:
Fall
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
FW 5550 - Geographic Information Science and Spatial Analysis
Use of geographic information systems (GIS) in resource management. Studies various components of GIS in detail, as well as costs and benefits. Laboratory exercises use ArcGIS software package to solve resource management problems.
Credits:
4.0
Lec-Rec-Lab:
(3-0-3)
Semesters Offered:
Fall, Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710
FW 5555 - Advanced GIS Concepts and Analysis
This course moves beyond the fundamentals of GIS to explore the application of GIS technology to environmental monitoring and resource management issues. Students learn graphic modeling techniques, network analysis, 3D visualization, geodatabase construction and management, and multivariate spatial analysis.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
FW 5550
FW 5556 - GIS Project Management
Course provides exposure to data collection techniques, web mapping applications, and advanced database structures. Students will investigate GIS system design, GIS project planning and data management, learn map atlas creation and cartographic techniques, and discuss geospatial ethics.
Credits:
3.0
Lec-Rec-Lab:
(1-0-4)
Semesters Offered:
Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
FW 5550
FW 5560 - Digital Image Processing: A Remote Sensing Perspective
Presents the theory and quantitative procedures of digital image processing using remotely sensed data. Emphasizes image acquisition, preprocessing, enhancement, transformation classification techniques, accuracy assessment, and out-products. Discusses linkages to GIS. Also covers evaluating applications of the technology to current resource management problems via peer-reviewed literature.
Credits:
4.0
Lec-Rec-Lab:
(3-0-1)
Semesters Offered:
Spring, in odd years
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
FW 5540
Geological and Mining Engineering and Sciences
GE 5150 - Advanced Natural Hazards
Exploration of how to develop comprehensive plans to mitigate the impact of natural hazards on humans. Requires a project and report.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
On Demand
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
GE 5195 - Volcano Seismology
Will prepare students, including those with no seismology background, to interpret seismic and acoustic signals from volcanoes. Topics: basic seismology, monitoring techniques, tectonic and volcanic earthquakes, infrasound, deformation over a range of time scales.
Credits:
3.0
Lec-Rec-Lab:
(2-0-1)
Semesters Offered:
Spring, in even years
Pre-Requisite(s):
(MA 1160 or MA 1161 or MA 1121 or MA 1135) and GE 2000 and PH 2100
GE 5215 - Time Series Analysis in Geosciences
Students will gain a solid foundation in time series analysis through theory and applications to geoscience data. Methods include regression, ARIMA models, linear Gaussian state-space models, and frequency-domain estimation. Work will be done in Python.
Credits:
3.0
Lec-Rec-Lab:
(3-0-0)
Semesters Offered:
Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
GE 5280
GE 5515 - Python for Geospatial Analysis
This advanced graduate-level course is designed to equip students with essential skills and techniques to perform geospatial data analysis using Python. This course emphasizes both theoretical concepts and hands-on practice, offering comprehensive insights into spatial data handling, visualization, statistical analysis, and application to real-world geospatial problems. Students will engage directly with real-world datasets through guided tutorials, comprehensive Jupyter notebook examples, and interactive classroom discussions.
Credits:
3.0
Lec-Rec-Lab:
(2-0-1)
Semesters Offered:
Spring
Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s):
GE 5280
GE 5600 - Advanced Reflection Seismology
Principles and application of reflection seismic techniques. Includes acquisition, data processing, and 2D/3D data interpretation. Project and report required.
Credits:
3.0
Lec-Rec-Lab:
(2-1-0)
Semesters Offered:
On Demand
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
GE 5870 - Geostatistics & Data Analysis
This course covers the handling of spatial and temporal data for knowledge discovery. Major topics include spatial interpolation, clustering, association analysis, and supervised and unsupervised classification. Students will learn how to use geostatistical and pattern recognition tools for geoscience applications.
Credits:
3.0
Lec-Rec-Lab:
(2-0-1)
Semesters Offered:
Spring, in odd years
Mathematics
MA 4720 - Design and Analysis of Experiments
Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The R programming language is an integral part of the course.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701
MA 5201 - Combinatorial Algorithms
Basic algorithmic and computational methods used in the solution of fundamental combinatorial problems. Topics may include but are not limited to backtracking, hill-climbing, combinatorial optimization, linear and integer programming, and network analysis.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, in even years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
MA 5221 - Graph Theory
Review of basic graph theory followed by one or more advanced topics which may include topological graph theory, algebraic graph theory, graph decomposition or graph coloring.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Fall, in odd years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
MA 5301 or MA 4209
MA 5627 - Numerical Linear Algebra
Design and analysis of algorithms for problems in linear algebra. Covers floating point arithmetic, condition numbers, error analysis, solution of linear systems (direct and iterative methods), eigenvalue problems, least squares, and singular value decomposition. Includes the use of appropriate software including high performance computational libraries.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring
Pre-Requisite(s):
MA 4330 or MA 4630
MA 5630 - Numerical Optimization
Numerical solution of unconstrained and constrained optimization problems and nonlinear equations. Topics include optimality conditions, local convergence of Newton and Quasi-Newton methods, line search and trust region globalization techniques, quadratic penalty and augmented Lagrangian methods for equality-constrained problems, logarithmic barrier method for inequality-constrained problems, and Sequential Quadratic Programming.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring, in even years
Pre-Requisite(s):
MA 4330 or MA 4610 or MA 4630 or MA 5627
MA 5750 - Statistical Genetics
Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
Spring, in odd years
MA 5771 - Applied Generalized Linear Models
Construction, evaluation, and application of generalized linear models to analyze different types of data. Topics include logistic and Poisson regression, multinomial logit models, random effects and mixed effect models, models for repeated measures and longitudinal data. Introduce theory on GLM fitting, hypothesis testing, and diagnostic models.
Credits:
3.0
Lec-Rec-Lab:
(0-3-0)
Semesters Offered:
On Demand
Pre-Requisite(s):
MA 4710 and (MA 4760 or (MA 4700 and MA 5701))
Physics
PH 4390 - Computational Methods in Physics
An overview of numerical, computer, and AI methods to analyze and visualize physics problems in mechanics, electromagnetism, and quantum mechanics. Utility and potential pitfalls of these methods, basic concepts of programming, computing environments, AI engines, system libraries and computer graphics are included.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
Fall
Pre-Requisite(s):
(PH 2021 or PH 2020) and PH 3410
PH 5396 - Statistics, Data Mining and Machine Learning in Astronomy
The course focuses on modern problem solving in Astronomy and Astrophysics through statistical inference, machine learning algorithms and data mining techniques. Students will be presented with data sets and research problems in astrophysics and will learn how to formulate solutions.
Credits:
3.0
Lec-Rec-Lab:
(2-0-3)
Semesters Offered:
Spring, in even years
Pre-Requisite(s):
PH 4390
Psychology and Human Factors
PSY 5220 - Advanced Statistical Analysis and Design II
Course covers multivariate statistics such as ANCOVA, Multiple Regression, factor analysis, clustering, machine learning, and mixture modeling.
Credits:
3.0;
Repeatable to a Max of 12
Lec-Rec-Lab:
(0-2-2)
Semesters Offered:
Spring, in odd years
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
PSY 5110
Co-op
UN 5001 - Advanced Responsible Conduct of Research
Three, 4 hour workshops on advanced responsible conduct of research. Covers topics necessary for this training, including ethical standards, publication practices, peer review process, conflict of interest and societal expectations.
Credits:
1.0
Lec-Rec-Lab:
(1-0-0)
Semesters Offered:
Summer
Restrictions:
Must be enrolled in one of the following Level(s): Graduate
UN 5002 - Graduate Cooperative Education I
Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.
Credits:
variable to 2.0
Semesters Offered:
Fall, Spring, Summer
Restrictions:
Permission of department required;
Must be enrolled in one of the following Level(s): Graduate
UN 5003 - Graduate Cooperative Education II
Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.
Credits:
variable to 2.0
Semesters Offered:
Fall, Spring, Summer
Restrictions:
Permission of department required;
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
UN 5002
UN 5004 - Graduate Cooperative Education III
Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.
Credits:
variable to 2.0;
May be repeated
Semesters Offered:
Fall, Spring, Summer
Restrictions:
Permission of department required;
Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s):
UN 5003
UN 5005 - Graduate Short Cooperative Education
Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.
Credits:
variable to 2.0;
May be repeated
Semesters Offered:
Summer
Restrictions:
Permission of department required;
Must be enrolled in one of the following Level(s): Graduate
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How to Apply
Application Process and Admissions Requirements
Applications are reviewed on an individual basis using a holistic approach. Fill out
our free
graduate application online
to apply to any of our programs. Official transcripts and scores are not required
for the initial application, although you will need to upload them later.
Graduate School Admissions Process
Applying to Graduate School is
free
(no application fees) and
fast
(no official transcripts or test scores are needed to start). The application process
involves three easy steps. International applicants are required to pay a non-refundable
$10 processing fee per application.
See Admissions Steps
Graduate School Admissions Options
Michigan Tech offers several admissions options in order to meet the educational needs
of students from a variety of backgrounds. Students should review the options available
to them and apply for the program that will best help them achieve their personal
educational goals.
See Admissions Options
Graduate School Requirements
To be considered for admission to the
Graduate School
as a degree- or certificate-seeking student, you need to:
have a bachelor's degree or its equivalent from an accredited institution, and
be prepared for advanced study in your chosen field, as demonstrated by your previous
degree and your scholastic record.
See
additional application requirements
, including required materials:
Student Statements
Official Transcripts
Program Specific Requirements
Program Specific
Any bachelor's degree which includes some coursework in introductory programming and
statistics
2 Letters of Recommendation
Résumé / Curriculum vitae
Admitted applicants typically have an undergraduate GPA of 3.0 or better on a 4.0
scale
GRE not required
No Additional Documents Accepted
International Students
TOEFL scores as of January 21, 2026: Recommended Score of 5
For tests taken before January 21, 2026: Recommended Score of 100 iBT
IELTS: Recommended Overall Band Score of at least 7.0
Michigan Tech
requires a minimum
4.5 overall TOEFL or 6.5 overall IELTS score.
Admissions Decisions
Made on a rolling basis.
Recommended Deadline
Apply at least a semester in advance of projected admission
Fall Semester: Recommended to apply by January 15
Spring Semester: Recommended to apply by October 15
International Student Requirements
International Students
must apply and be accepted
into a degree-granting program in order to earn a graduate certificate.
A non-refundable $10 processing fee per application is required.
See International Applicants
Accelerated Master's Requirements
Our
Accelerated Master's Program
is available for current Michigan Tech students.
Program Specific
2 Letters of Recommendation
GPA of 3.0 or greater
No GRE required
Résumé/ Curriculum vitae
Eligible Undergraduate Majors
Computer Science
Cybersecurity
Software Engineering
Computer Engineering
Bioinformatics
Cheminformatics
Mathematics and Computer Science
Mathematics - Applied and Computational
Mathematics - Business Analytics
Other related majors with computational and statistics background upon approval
Accredited by HLC
Michigan Tech has been
accredited
by the Higher Learning Commission (HLC) since 1928. Our Graduate School offers over
125 certificates, master's, and PhD programs to provide our students and the world
with what tomorrow needs.
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For Prospective Students
"We don't just teach the technical fundamentals like artificial intelligence, machine
learning, data analytics and data wrangling as abstract tools; we pair them directly
with real-world context. Whether our students are diving into advanced manufacturing,
the EV (electric vehicle) industry or health sciences, they aren't just 'processing
data.' They are learning the deep context of the problems they're solving.
Dennis Livesay, Dave House Dean of Computing