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Biostatistics is an innovative field that involves the design, analysis, and interpretation of data for studies in public health and medicine. Biostatistics experts arrive play a crucial role in drawing meaningful at conclusions about disease and health risks by evaluating and applying employing mathematical and statistical formulas to address the factors that impact health.

Students at Pitt Public Health our students benefit from collaboration with the renowned University of Pittsburgh School of Medicine and UPMC, Pennsylvania’s largest academic medical center, and have access to extensive University computing facilities. Through rigorous courses, students gain a comprehensive understanding of statistical methods in the context of public health and medical problems; work with faculty on developing new and innovative methodologies and analytical techniques; and have the opportunity through both research and service to apply these methods to current and pressing concerns in biomedicine and public health.

Contact Information

General Inquiries
biostat@pitt.edu

Admissions

In addition to the School-level Admission Requirements , admission to all Biostatistics degree programs requires two semesters of calculus.

Applying for Admission to Pitt Public Health  

Early Admission

University of Pittsburgh undergraduates may apply for early admission to the Biostatistics MS Program. Students must have completed the number of undergraduate credits required by their school before they can become a Biostatistics MS student.

Accelerated Bachelor’s/Master’s Program   

Accelerated Bachelor’s/Master’s Program Admission Requirements  

Financial Aid

The department awards assistants to full-time PhD students. The department awards financial aid to applicable MS students at the time of admission.

Tuition and Billing  

Tuition & Financial Aid  

Advanced Standing and Credit Transfer

Acceptance of a maximum of 12 credits taken as a non-degree student at Pitt Public Health is at the discretion of the department and program.

Students enrolled at Pitt Public Health may take credits in another school or institution, providing that their department has approved application of those credits to the degree requirements. In all cases, any combination of advanced standing credits and credits taken from another school or institution during enrollment at Pitt Public Health may not exceed the limits established by the University or the Regulations Governing Graduate Study at the University of Pittsburgh .

Students with previous graduate experience in Biostatistics or a related field may apply to transfer credits for graduate-level coursework successfully completed with a grade of B or better.  PhD students may apply to transfer up to 24 credits and MS students 6 credits. The course credits to be transferred must be reviewed and approved by the student’s academic advisor. Students who receive transfer credits for a public health core course must complete a Core Course Exemption Form in addition to the credit transfer paperwork to exempt out of those classes. Students who receive transfer credits for BIOST 2087  must complete a Course Exemption Form in addition to the credit transfer paperwork to exempt out of the course. All transfer credit and course exemption paperwork must be completed by the end of a student’s first term of study.

Programs

• Biostatistics, PhD

• Biostatistics, MS
• Biostatistics, MS - Health Data Science (HDS) Concentration
• Biostatistics, MS - Statistical and Computational Genomics (SCG) Concentration

Courses

• BIOST 2000 - TEACHING PRACTICUM
• BIOST 2011 - PRINCIPLES OF STATISTICAL REASONING
• BIOST 2016 - SAMPLING DESIGN AND ANALYSIS
• BIOST 2021 - SPECIAL STUDIES
• BIOST 2022 - CAPSTONE PREPARATION
• BIOST 2022 - CAPSTONE PREPARATION
• BIOST 2025 - BIOSTATISTICS SEMINAR
• BIOST 2036 - INTRODUCTION TO HEALTH DATA SCIENCE
• BIOST 2037 - FOUNDATIONS OF STATISTICAL THEORY
• BIOST 2038 - FOUNDATIONS OF STATISTICAL THEORY
• BIOST 2039 - BIOSTATISTICAL METHODS
• BIOST 2040 - ELEMENTS OF STOCHASTIC PROCESSES
• BIOST 2041 - INTRODUCTION TO STATISTICAL METHODS
• BIOST 2042 - INTRODUCTION TO STATISTICAL METHODS 2
• BIOST 2043 - INTRODUCTION TO STATISTICAL THEORY 1
• BIOST 2044 - INTRODUCTION TO STATISTICAL THEORY 2
• BIOST 2046 - ANALYSIS OF COHORT STUDIES
• BIOST 2049 - APPLIED REGRESSION ANALYSIS
• BIOST 2050 - LONGITUDINAL AND CLUSTERED DATA ANALYSIS
• BIOST 2051 - STATISTICAL ESTIMATION THEORY
• BIOST 2052 - MULTIVARIATE ANALYSIS
• BIOST 2054 - SURVIVAL ANALYSIS
• BIOST 2056 - STATISTICAL EVALUATION OF BIOMARKERS AND CLASSIFICATION TOOLS
• BIOST 2058 - SCIENTIFIC COMMUNICATION SKILLS
• BIOST 2059 - CONSTRAINED STATISTICAL INFERENCE WITH APPLICATIONS
• BIOST 2061 - LIKELIHOOD THEORY AND APPLICATION
• BIOST 2062 - CLINICAL TRIALS: METHODS AND PRACTICE
• BIOST 2063 - BAYESIAN DATA SCIENCE
• BIOST 2065 - ANALYSIS OF INCOMPLETE DATA
• BIOST 2066 - APPLIED SURVIVAL ANALYSIS: METHODS AND PRACTICE
• BIOST 2067 - APPLIED META-ANALYSIS
• BIOST 2068 - INTRODUCTION TO CASUAL INFERENCE
• BIOST 2068 - INTRODUCTION TO CAUSAL INFERENCE
• BIOST 2069 - STATISTICAL METHODS FOR OMICS DATA
• BIOST 2069 - STATISTICAL METHODS FOR OMICS DATA
• BIOST 2079 - INTRODUCTORY STATISTICAL LEARNING FOR HEALTH SCIENCES
• BIOST 2080 - ADVANCED STATISTICAL LEARNING
• BIOST 2080 - ADVANCED STATISTICAL LEARNING
• BIOST 2081 - MATHEMATICAL METHODS FOR STATISTICS
• BIOST 2083 - LINEAR MODELS
• BIOST 2086 - MIXED MODELS
• BIOST 2087 - BIOSTATISTICS CONSULTING PRACTICUM
• BIOST 2093 - SAS FOR DATA MANAGEMENT AND ANALYSIS
• BIOST 2094 - ADVANCED R COMPUTING
• BIOST 2096 - NUMERICAL METHODS BIOSTATISTICS
• BIOST 2099 - CAPSTONE
• BIOST 2137 - FOUNDATIONS OF STATISTICAL THEORY
• BIOST 3010 - RESEARCH AND DISSERTATION PHD

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