Online Master of Science in Applied Statistics (MS)

Apply Statistics to Data-Driven Decisions.

Applied Statistics is the implementation of statistical methods, techniques, and theories to real-world problems and situations in several fields, such as science, engineering, business, medicine, social sciences, and more.

This important discipline involves collecting, summarizing, analyzing, interpreting, and presenting data to make informed decisions, solve problems, and answer questions.

Gut Vs. Data: The Importance of Applied Statistics

With Michigan Tech's Online MS in Applied Statistics, you too can build the specialized skills to meet the growing demand for statistics and data professionals.

The online MSAS curriculum is based on direct industry feedback. Through in-depth coursework, you will learn how to merge theory and application, enabling you to graduate ready to address the data-driven challenges that public, private, and non-governmental institutions face. You'll develop expertise in several programming languages and tools that applied statisticians regularly use on the job.

Logo for R

Who is The Online MS in Applied Statistics For?

The MSAS degree is designed for professionals who use data—or want to use it more effectively—in business, science, engineering, healthcare, government, or research. It may also suit analysts who want stronger statistical foundations, technical professionals who need more advanced modeling skills, and career changers preparing for data-focused responsibilities.

Prospective students should have a strong knowledge of Linear Algebra and be comfortable working with statistical software and developing quantitative and computing skills.

How the Program Works

The online MS in Applied Statistics consists of compact, 7-week courses, which are offered in at least two semesters.

But before making your plan, please check the courses and registration page to see what classes are currently running.

MA 4700 - Probability and Statistical Inference I

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)

MA 4705 - Probability and Statistical Inference II

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

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 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 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 5751 - Statistical Data Mining

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)

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 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))

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 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)

Why Choose Michigan Tech?

Michigan Tech's online MS in Applied Statistics brings together industry-informed coursework, applied statistical methods, and the programming tools used in data-focused careers. The curriculum, which was developed with direct industry feedback, is delivered in compact seven-week courses, so you can earn your degree more quickly.

You'll learn from Mathematical Sciences faculty whose work applies quantitative methods to challenging questions across disciplines, such as bioinformatics, econometrics, and statistical genetics.

3semesters in which courses are regularly offered

30credits to complete the degree

1.84years = the median time to complete the master's degree

78%of students completed the program within 1.67 years

Where You'll Apply Your Knowledge and Skills

Applied statisticians use advanced techniques, such as machine learning algorithms, to extract insights and patterns from large datasets. Experts in applied statistics work in several settings: research institutions, the government, business and finance, universities, healthcare systems, and more.

Some of our current graduates are actuarial analysts, data analysts, data scientists, financial planners, math teachers, quality analysts, software engineers, and statisticians.

Three professionals work out an Applied Statistics problem on a clear board.

Discover Diverse Careers in Applied Statistics

Learn About Sports Statisticians

Possible Career Paths

Healthcare and Pharmaceuticals: Hospitals, public health organizations, biotech companies, and pharmaceutical firms use statisticians for clinical trials, epidemiology, and health data analysis.
Finance and Insurance: Banks, investment firms, and insurance companies hire statisticians for risk analysis, fraud detection, and forecasting.
Technology and Data Analytics Companies: Tech giants, data analytics consultancies, and software firms need statisticians for machine learning, AI, and big data projects.
Academia and Research Institutions. Universities, think tanks, and private research labs employ statisticians for teaching, methodological research, and collaborative projects.
Government Agencies: The US Census Bureau, Bureau of Labor Statistics, Centers for Disease Control and Prevention (CDC), and National Institutes of Health (NIH) have hired statistics experts.
Sports Organizations: Professional teams, leagues, and sports analytics firms use statisticians to evaluate player performance, develop game strategies, assess injury risks, and analyze team and league-wide trends to gain a competitive edge.

Your Program Questions, Answered

Watch this webinar recording below to learn more about the Online MS in Applied Statistics.

Preview image for Michigan Tech's Online MS of Applied Statistics Webinar (Fall 2025) video

Video Guide

Introductions: 00:01 - 07:01
What is Applied Statistics?: 07:02 - 10:58
Why Study at MTU?: 11:01 - 17:27
Program Overview: 17:34 - 30:35
Career Outcomes: 30:36 - 34:04
Application and Next Steps: 34:06 - 40:37
Questions: 40:38 - 47:01

Get Started on Your Online MS in Applied Statistics.

Open up more opportunities to exciting, data-driven careers by earning an online master's degree in applied statistics from Michigan Tech.

Take the next steps on your online educational journey at Michigan Tech. Reach out when you're ready. Or start your free, easy online graduate application.