Twin Cities campus
Twin Cities Campus

Applied Biostatistics Postbaccalaureate Certificate

School of Public Health - Adm
School of Public Health
Link to a list of faculty for this program.
Contact Information
School of Public Health, MMC 819, A395 Mayo Memorial Building, 420 Delaware St, Minneapolis, MN 55455 (612-626-3500 OR 1-800-774-8636)
  • Program Type: Post-baccalaureate credit certificate/licensure/endorsement
  • Requirements for this program are current for Fall 2019
  • Length of program in credits: 15
  • This program requires summer semesters for timely completion.
  • Degree: Applied Biostatistics PBacc Cert
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
This primarily online certificate program is designed for working biostatisticians, such as data managers and analysts, who are not formally trained and want to improve their technical, mathematical, and computational skills. The certificate focuses on key aspects of study design, implementation, and analysis for observational and clinical studies.
This program is accredited by Council on Education for Public Health (CEPH)
Program Delivery
  • primarily online (at least 80% of the instruction for the program is online with short, intensive periods of face-to-face coursework)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
Other requirements to be completed before admission:
Admission preferences and prerequisites: - Bachelor's degree - Strong GPA in math and science coursework - Strong written skills - Work experience
Special Application Requirements:
Applicants must submit to SOPHAS Express, a centralized online application service: - Completed SOPHAS Express application and application fee, designating the University of Minnesota School of Public Health - Personal statement describing the applicant's reason for applying, career goals, and how the certificate will help them achieve their goals - One letter of recommendation -Unofficial transcripts of record from each college/university where a degree was earned. (If admitted, official transcripts will need to be sent directly to the School of Public Health.) -Resume or C.V. For detailed application requirements and instructions visit
International applicants must submit score(s) from one of the following tests:
    • Internet Based - Total Score: 100
    • Paper Based - Total Score: 600
    • Total Score: 7.0
Key to test abbreviations (TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.00 is required for students to remain in good standing.
Courses must be taken A-F, unless offered only S-N. The minimum grade for each A-F graded course is B-.
Required Coursework (15 credits)
Select PubH 6431 or PubH 6432 in consultation with the advisor.
PUBH 6450 - Biostatistics I (4.0 cr)
PUBH 6451 - Biostatistics II (4.0 cr)
PUBH 6320 - Fundamentals of Epidemiology (3.0 cr)
PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
PUBH 6431 - Topics in Hierarchical Bayesian Analysis (1.0 cr)
or PUBH 6432 - Biostatistical Methods in Translational and Clinical Research (1.0 cr)
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View college catalog(s):
· School of Public Health

View future requirement(s):
· Fall 2020

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PUBH 6450 - Biostatistics I
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Descriptive statistics. Gaussian probability models, point/interval estimation for means/proportions. Hypothesis testing, including t, chi-square, and nonparametric tests. Simple regression/correlation. ANOVA. Health science applications using output from statistical packages. prereq: [College-level algebra, health sciences grad student] or instr consent
PUBH 6451 - Biostatistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Two-way ANOVA, interactions, repeated measures, general linear models. Logistic regression for cohort and case-control studies. Loglinear models, contingency tables, Poisson regression, survival data, Kaplan-Meier methods, proportional hazards models. prereq: [PubH 6450 with grade of at least B, health sciences grad student] or instr consent
PUBH 6320 - Fundamentals of Epidemiology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.
PUBH 7415 - Introduction to Clinical Trials
Credits: 3.0 [max 3.0]
Course Equivalencies: 02819
Typically offered: Every Fall & Summer
Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent
PUBH 6431 - Topics in Hierarchical Bayesian Analysis
Credits: 1.0 [max 1.0]
Grading Basis: OPT No Aud
Typically offered: Every Summer
Hierarchical Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing.
PUBH 6432 - Biostatistical Methods in Translational and Clinical Research
Credits: 1.0 [max 1.0]
Grading Basis: OPT No Aud
Typically offered: Periodic Summer
This short course on translational and clinical research will focus on the topics of diagnostic medicine and designing clinical research methods, application of regression models and early phase clinical trials. prereq: Students will benefit from having taken one or two semester courses in biostatistics or applied statistics covering up to and including multiple regression and introductory logistic regression.