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Master's in Health Informatics

Health Informatics students are from a wide range of backgrounds, including medical/clinical, management information systems, kinesiology, integrated physiology, bio/chem-informatics, computer science, cybersecurity, biomedical engineering, and more.

A Flexible, Stackable Master's Program

The Health Informatics graduate program requires 30 credits of course work and/or research for the MS degree. Students who successfully complete the required 30 credits of course work will earn an MS in Health Informatics degree plus 3 graduate certificates (1 foundational certificate + 2 focus area certificates).

You'll begin with the 12-credit Foundations of Health Informatics certificate, then complete two of the three focus-area certificates, below. A custom plan of study for is formulated for each individual as they enter the program.

MS Requirements

Health Informatics Graduate Foundations Courses (12 credits)
Focus Areas (9-18 credits)
Elective courses or Report option or Thesis option (6-10 credits)

Completion Options

MS, Health Informatics: 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)	12 Credits
3000-4000 (maximum)	12 Credits

Programs may have stricter requirements and may require more than the minimum number of credits listed here.

MS, Health Informatics: 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 twelve 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)	12 Credits
3000-4000 (maximum)	12 Credits

Programs may have stricter requirements and may require more than the minimum number of credits listed here.

MS, Health Informatics: 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

Elective Courses

A maximum of two courses or six credits may be used for elective credit.

Any SAT 5000 level course not taken from above and the following.

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

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)

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): Business Administration, Data Science, Applied Natural Resource Econ., Health Informatics, Accounting, Engineering Management

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

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)

KIP 4740 - Epidemiology

An introduction to the principles and methods of epidemiology to understand the distribution and determinants of health in a population. Topics include basic epidemiological statistics, study design, and sources/impact of bias and error.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring

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 or CS 1121

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

2 Letters of Recommendation
Résumé / Curriculum vitae
Admitted applicants typically have an undergraduate GPA of 3.0 or better on a 4.0 scale
Prerequisites: Statistics, Principles of Economics
Recommended: Finance and Accounting
GRE/GMAT not required
No Additional Documents Accepted

International Students

TOEFL: Recommended Score of at least 95 iBT
IELTS: Recommended Overall Band Score of at least 7.0
English Proficiency is required for all applicants who do not come from the US, Canada, UK, Australia, and New Zealand

Michigan Tech requires a minimum 79 overall TOEFL or 6.5 overall IELTS score.

Admissions Decisions

Made on a rolling basis

Recommended Deadlines

Fall Semester: July 1
Spring Semester: December 1

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 Health Informatics Admissions Requirements

Program Specific

3 Letters of Recommendation
Overall GPA of 3.0 or greater
No GRE required

Eligible Undergraduate Majors

Computer Network and System Administration

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Eligibility: An undergraduate degree in mechanical engineering or a closely related technical field from a regionally accredited academic institution with a cumulative GPA of 3.00 or higher on a 4.0 scale.

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