Online Masters of Applied Statistics (MS) | Michigan Tech Global Campus

Solve Real-World Problems With Applied Statistics.

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.

Applied statisticians also 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.

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. MTU's distance-learning, research-based MS combines tested techniques with emerging technologies while maintaining the high standards of a Michigan Tech graduate education.

Get the Details About the Online MS in Applied Statistics.

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Overview of the Applied Statistics Program.

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

The program consists of compact, 7-week courses, which are offered in at least two semesters.

Depending on what courses you take, the availability of those courses, and how many courses you complete in a semester, you may be able to earn your graduate degree quickly. That is, if you took two courses per semester, you could earn your MS in five semesters.

Whatever courses you decide upon, you'll be taught by expert faculty from the Department of Mathematical Sciences who conduct research on the most effective mathematical and statistical approaches to diverse problems. You'll receive the same quality instruction as do students in MTU's on-site classes.

Need to brush up on your Linear Algebra? No problem. MTU offers an affordable Linear Algebra Bridge Course for those who want to apply to (or are provisionally accepted into) the Online MSAS degree.

Explore the Curriculum of the Online MSAS.

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
Restrictions: Permission of department required
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, Summer
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, Summer
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)

Get Applied Statistics Skills for Several Rewarding Careers.

With MTU's Online MS in Applied Statistics, you'll develop skills for a wide range of industries and sectors where data analysis and statistical methods are implemented to make decisions, solve problems, and improve processes.

Top Six Employers of Applied Statisticians

Three applied statistics professionals solving a problem on a board.

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 with other disciplines.
Government Agencies: US Census Bureau, Bureau of Labor Statistics, Centers for Disease Control and Prevention (CDC), National Institutes of Health (NIH), Department of Agriculture 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.

Learn More About Applied Statistics Careers

Employer Spotlight: Healthcare and Pharmaceuticals

Although there are several career paths, a degree in applied statistics is one of the most direct pathways into the healthcare and pharmaceutical industries. Why? These sectors increasingly depend on data-driven decision-making. From designing and analyzing clinical trials to evaluating patient outcomes, statisticians play a pivotal role in advancing medical research and improving public health.

And the demand for skilled data analysts, medical statisticians, and biostatisticians continues to grow due to expanding health datasets, stricter regulatory requirements, and the push for evidence-based medicine.

Explore the accordion slider below to learn more about these types of employers and the associated job tasks.

Healthcare Systems and Academic Medical Centers

Typical work: outcomes research, population health analytics, quality improvement, health services research.
Examples: Mayo Clinic, Cleveland Clinic, Kaiser Permanente, Johns Hopkins Medicine.

Biotechnology Firms

Typical work: biomarker development, genomics/proteomics analytics, early-stage therapeutic studies.
Examples: Genentech, Gilead Sciences, Biogen

Pharmaceutical Companies

Typical work: clinical trial design/analysis, regulatory submissions, drug safety and efficacy.
Examples: Pfizer, Merck & Co., Johnson & Johnson, Eli Lilly, Bristol Myers Squibb

Healthcare Data Analytics

Typical work: outcomes research, population health analytics, quality improvement, health services research.
Examples: IQVIA, Parexel, PPD (Thermo Fisher Scientific), ICON plc.

Contract Research Organizations (CROs)

Typical work: end-to-end clinical trial statistics for pharma/biotech clients, data management, biostatistics consulting.
Examples: IQVIA, Parexel, Thermo Fisher Scientific

Federal Health Agencies

Typical work: epidemiology, public health surveillance, clinical research support.
Examples: Centers for Disease Control and Prevention (CDC), National Institutes of Health (NIH).

How to Become a Medical Statistician

Read What Previous Graduates Have Said.

"I chose Michigan Tech because I am a high school math teacher and I was looking for a program in statistics that was 100% online. I was aware of Michigan Tech's reputation and knew they were a solid school in mathematical fields. I also like that no GRE was required for admission."Mary Lonz, High School Math Teacher, MS in Applied Statistics

"The fact that the courses cover multiple applications is what stood out to me. It shows MTU has done adequate research to ensure students succeed post their degree. The professors provide feedback to our discussion posts in a timely manner. I feel they are there for you whenever you need them."Jay Huen Lee, Senior Analyst/Commercial Analytics, MS in Applied Statistics

Get Started on Your Online MS in Applied Statistics!

Open up more opportunties 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 to our Graduate Admissions Manager, Amanda, who can help you decide if this program is right for you. Or hit our RFI button below.

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