Our online classes are regular Michigan Tech classes available to anyone qualified to take classes at Tech, anywhere in the world. Students earn course credit, the same as any on-campus class. Students must meet the standard prerequisites for each course. Registration for 4000-level and 5000-level courses may require prior departmental approval.
Summer 2026
Continued study of calculus, which includes a computer laboratory. Topics include integration and its uses, function approximation, vectors, and elementary modeling with differential equations.
- Credits: 4.0
- Lec-Rec-Lab: (0-3-1)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 1160 or MA 1161 or MA 1135 or MA 1121 or CEEB Calculus AB >= 3 or CEEB Calculus BC >= 3 or CEEB Calculus AB Subscore >= 3
An introduction to linear algebra and how it can be used. Topics include systems of equations, vectors, matrices, orthogonality, subspaces, and the eigenvalue problem.
- Credits: 2.0
- Lec-Rec-Lab: (0-2-0)
- Semesters Offered: Fall, Spring, Summer
- Restrictions: May not be enrolled in one of the following Major(s): Mathematics, Software Engineering, Computer Science
- Pre-Requisite(s): MA 1160 or MA 1161 or MA 1135 or MA 1121
Introduction to the design and analysis of statistical studies. Topics include methods of data collection, descriptive and graphical methods, probability, statistical inference on means, regression and correlation, and ANOVA. The course will include an introduction to statistical software.
- Credits: 4.0
- Lec-Rec-Lab: (0-4-0)
- Semesters Offered: Fall, Spring, Summer
- Restrictions: May not be enrolled in one of the following Major(s): Mathematics
- Pre-Requisite(s): MA 1020 or MA 1030 or MA 1120 or MA 1032 or MA 1031 or ALEKS Math Placement >= 61 or CEEB Calculus BC >= 2 or CEEB Calculus AB Subscore >= 2 or ACT Mathematics >= 22 or SAT MATH SECTION SCORE-M16 >= 540
Introduction to calculus in two and three dimensions, which includes a computer laboratory. Topics include functions of several variables, partial derivatives, the gradient, multiple integrals; introduction to vector-valued functions and vector calculus, divergence, curl, and the integration theorems of Green, Stokes, and Gauss.
- Credits: 4.0
- Lec-Rec-Lab: (0-3-1)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 2160 or CEEB Calculus BC >= 3
Topics include private-key cryptography, shift substitution, permutation and stream ciphers, cryptanalysis, perfect secrecy, public-key cryptography, and the RSA cryptosystem.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring, Summer
- Pre-Requisite(s): MA 2320 or MA 2321 or MA 2330
First order equations, linear equations, and systems of equations.
- Credits: 2.0
- Lec-Rec-Lab: (0-2-0)
- Semesters Offered: Fall, Spring, Summer
- Restrictions: May not be enrolled in one of the following Major(s): Mathematics, Computer Science
- Pre-Requisite(s): MA 2160 and (MA 2320 or MA 2321 or MA 2330)
Introduction to the design, conduct, and analysis of statistical studies aimed at solving engineering problems. Topics include methods of data collection, descriptive and graphical methods, probability and probability models, statistical inference, control charts, linear regression, design of experiments.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 2160 or MA 3160(C)
Introduction to probabilistic methods. Topics include probability laws, counting rules, discrete and continuous random variables, moment generating functions, expectation, joint distributions, and the Central Unit Theorem.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 3160 and (MA 2710 or MA 2720 or MA 3710 or MA 3715)
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
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)
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)
Spring 2026
An introduction to linear algebra and how it can be used. Topics include systems of equations, vectors, matrices, orthogonality, subspaces, and the eigenvalue problem.
- Credits: 2.0
- Lec-Rec-Lab: (0-2-0)
- Semesters Offered: Fall, Spring, Summer
- Restrictions: May not be enrolled in one of the following Major(s): Mathematics, Software Engineering, Computer Science
- Pre-Requisite(s): MA 1160 or MA 1161 or MA 1135 or MA 1121
First order equations, linear equations, and systems of equations.
- Credits: 2.0
- Lec-Rec-Lab: (0-2-0)
- Semesters Offered: Fall, Spring, Summer
- Restrictions: May not be enrolled in one of the following Major(s): Mathematics, Computer Science
- Pre-Requisite(s): MA 2160 and (MA 2320 or MA 2321 or MA 2330)
Introduction to the design, conduct, and analysis of statistical studies aimed at solving engineering problems. Topics include methods of data collection, descriptive and graphical methods, probability and probability models, statistical inference, control charts, linear regression, design of experiments.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 2160 or MA 3160(C)
Introduction to probabilistic methods. Topics include probability laws, counting rules, discrete and continuous random variables, moment generating functions, expectation, joint distributions, and the Central Unit Theorem.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, Spring, Summer
- Pre-Requisite(s): MA 3160 and (MA 2710 or MA 2720 or MA 3710 or MA 3715)
Topics include sampling distributions, theory of point and interval estimation, properties of estimators, and theory of hypothesis testing.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: On Demand
- Restrictions: Permission of department required
- Pre-Requisite(s): MA 4700
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
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
Course will cover various topics in statistical data mining, including linear model selection and regularization, regression and smoothing splines, unsupervised learning, resampling methods, tree-based methods, and deep learning. This course will introduce modern statistical data mining techniques and their applications.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring, in odd years
- Pre-Requisite(s): (MA 4700 or MA 4760) and (MA 5701 or MA 4710)
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)