Program Overview

Develop the skills to become a public health data scientist and improve healthcare and population health with the Public Health Data Science MS in Temple’s College of Public Health. This 36-credit graduate program is designed to equip graduates to meet the growing demand for data scientists who have an in-depth understanding of biostatistical methods and data analytics.

As a student, you will learn to conceptualize health problems and use state-of-the-art tools and techniques to analyze, design and manage health and health-related data to produce value-adding analytic insights. You will also learn how to effectively combine and communicate these insights to inform evidence-based public health decision-making.

Graduates of this program will have a strong biostatistical and programming foundation, a mastery of targeted data analysis, and be prepared to apply their skills as follows.

• Apply appropriate statistical methods for summarizing complex public health data, such as estimation, confidence intervals and hypothesis testing.
• Determine distinct measurement scales and recognize the implications for the selection of appropriate statistical methods.
• Distinguish statistical models with respect to data structure.
• Identify appropriate methods for data collection issues.
• Interpret and present results to professional and lay audiences.
• Recognize the concepts of probability, commonly used statistical probability distributions and random variation.
• Reveal patterns by employing efficient, fluent programming skills in conjunction with statistical inference and big data.
• Understand how experimental design influences statistical inference and uncertainty.
• Utilize appropriate machine learning algorithms that incorporate uncertainty.

Graduates of the program will also be prepared to analyze and utilize “big health data” or “complex study designs” to improve health outcomes while lowering costs, leveraging multiple data sources for infectious disease surveillance and other newly emerging issues.