MS - HDS Concentration

The MS in Biostatistics with area of concentration in Health Data Science is designed for students with a background in math, some experience with a programming language, and a strong interest in public health and data science. The HDS concentration emphasizes biostatistical theory and statistical computational methods for analyzing, processing and interpreting large-scale data sets so that students are prepared to clean, store, manage, manipulate, visualize and process high dimensional data as well as be effective statistical collaborators in interdisciplinary studies; and lead the design and execution of studies.

Biostatistics and HDS Careers

Addressing the rising need for health care analytics, our HDS concentration provides cross-disciplinary and necessary training for graduates of our program to be in high demand. In fact, Glassdoor ranks data scientist as the #1 best job in America for 2019 and Forbes magazine states “IBM Predicts Demand For Data Scientists Will Soar 28% by 2020. Here are just a few employers with open positions for health data scientists found on a recent search on Indeed, Glassdoor and ZipRecruiter:

  • Amazon
  • Fortive
  • GNC
  • Google
  • Highmark Health
  • Innovu
  • RAND
  • Thermo Fisher Scientific

Program Information

MS-HDS Schedule (PDF, 2023-24)
MS-HDS Degree Requirements Worksheet (PDF, 2023-24)
Student Handbook (PDF, 2023-24)

Admissions

Biostatistics News

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Sample Thesis Titles

Browse titles in D-Scholarship, the institutional repository for research output at the University of Pittsburgh

"The proliferation of master’s and doctoral programs in data science and analytics continues, seemingly due to the insatiable demand of employers for data scientists." - Amstat News, 2019

HDS Concentration-specific Competencies
  • Identify appropriate problem definitions, study designs, and data collection methods to address public health problems
     
  • Utilize fundamental theoretical concepts and relationships to effectively apply and interpret common statistical inference techniques
     
  • Use common biostatistical inference techniques and regression models to analyze data and interpret the results for public health practice
  • Recognize strengths and weaknesses of approaches, including alternative designs, data sources, and analytic methods
     
  • Communicate the meaning, potential, and results of biostatistical analyses to potential collaborators with varying degrees of statistical knowledge
     
  • Effectively use R software for basic statistical analysis and advanced programming tasks
  • Apply data curation, wrangling, and management techniques such as data munging, data scraping, sampling, and cleaning to construct informative, usable, and manageable data sets for meaningful analyses
     
  • Apply methods for big data, including supervised and unsupervised machine learning to reveal patterns, trends and associations including visualization
     
  • Apply advanced methods in at least three major areas of data science

Requirements

40 credits of coursework in:

  • Fundamentals of statistical theory, applications, epidemiology and public health
  • Software for statistical analysis and programming (R and electives)
  • Data curation, wrangling and management
  • Methods for big data and machine learning
  • Advanced methods in several major areas of data science

Finalized with a statistical consulting practicum and MS thesis project.

Professor talking to students