Program: Biostatistics, MS - Health Data Science (HDS) Concentration - University of Pittsburgh

Required Courses

BIOST 2025 - BIOSTATISTICS SEMINAR
BIOST 2081 - MATHEMATICAL METHODS FOR STATISTICS
BIOST 2131 - FOUNDATIONS OF STATISTICAL THEORY
BIOST 2141 - BIOSTATISTICAL METHODS
BIOST 2142 - APPLIED REGRESSION ANALYSIS
BIOST 2181 - HEALTH DATA SCIENCE: DATA MANAGEMENT AND PROGRAMMING
BIOST 2155 - INTRODUCTORY STATISTICAL LEARNING FOR HEALTH SCIENCES
OR
BIOST 2183 - HEALTH DATA SCIENCE: STATISTICAL LEARNING AND ARTIFICIAL INTELLIGENCE
BIOST 2182 - HEALTH DATA SCIENCE: DATA VISUALIZATION AND BIG DATA
BIOST 2179 - BIOSTATISTICS CONSULTING PRACTICUM
EPIDEM 2110 - PRINCIPLES OF EPIDEMIOLOGY
PUBHLT 2011 - ESSENTIALS OF PUBLIC HEALTH
PUBHLT 2022 - THE DEAN’S PUBLIC HEALTH GRAND ROUNDS (two terms)
BIOST 2198 - MS THESIS PREPARATION
FOLLOWED BY (in the next term)
BIOST 2199 - MS THESIS

Elective Courses

Students add electives from the list below to earn total credits required for the degree program. In situations where a student’s special interests or needs indicate an alternative course is more appropriate it may be substituted with the permission of the student’s academic advisor. BIOST 2025 cannot be used to fulfill elective credits.

BCHS 2551 - SOCIAL NETWORKS & HEALTH
BCHS 3021 - SOCIAL NETWORKS AND HEALTH II: COMPUTER METHODS
BIOST 2080 - ADVANCED STATISTICAL LEARNING
BIOST 2151 - BAYESIAN DATA SCIENCE
BIOST 2173 - SAS FOR DATA MANAGEMENT AND ANALYSIS
BMIS 2542 - DATA PROGRAMMING ESSENTIALS WITH PYTHON
BMIS 2588 - DATA BASE MANAGEMENT
COBB 2060 - MACHINE LEARNING FOR BIOMEDICAL APPLICATIONS
INFSCI 2160 - DATA MINING
INFSCI 2410 - INTRODUCTION TO NEURAL NETWORKS
INFSCI 2591 - ALGORITHM DESIGN
INFSCI 2595 - MACHINE LEARNING
INFSCI 2710 - DATABASE MANAGEMENT
INFSCI 2725 - DATA ANALYTICS (Prior R, Java, or Python programming experience required)
PHARM 5834 - PYTHON FOR DATA MANAGEMENT AND ANALYTICS
STAT 2240 - APPLIED SAMPLING
STAT 2270 - DATA MINING
STAT 2280 - DATA SCIENCE WITH PYTHON
STAT 2310 - APPLIED MULTIVARIATE ANALYSIS

Other Requirements

Comprehensive Examination

MS students must pass a written comprehensive examination offered annually at the end of the first year of study in early May. The MS comprehensive examination covers applied methods as well as theoretical concepts in a 3-hour exam.  The examination is a proctored closed book exam.

A student is eligible to take the comprehensive examination if the student:

is enrolled in the MS Program in Biostatistics Health Data Science concentration with good standing (3.00 QPA or greater)
did not fail the comprehensive examination more than once
completed the required courses (listed below) with a B or better, or equivalent coursework which the student has obtained transfer credits or exemption
BIOST 2131 FOUNDATIONS OF STATISTICAL THEORY
BIOST 2141 BIOSTATISTICAL METHODS
BIOST 2142 APPLIED REGRESSION ANALYSIS
EPIDEM 2110 PRINCIPLES OF EPIDEMIOLOGY

Oral Examination - Thesis Defense

After successful completion of the MS Comprehensive Examination requirement, MS students must complete for two semesters (three credits total) of faculty-guided thesis research (BIOST 2198 and BIOST 2199). Thesis work includes analysis, writing, and defending a master’s thesis.

Biostatistics, MS - Health Data Science (HDS) Concentration

Contact Information

Required Courses

Elective Courses

Other Requirements

Comprehensive Examination

Oral Examination - Thesis Defense

Total Credits: 40