Program Overview
The University of South Carolina's Biostatistics, Ph.D. program equips students with advanced biostatistical techniques to investigate health conditions and analyze health programs. Graduates pursue careers in academia, research, or industry, where they use data to address complex health issues in diverse settings such as education, healthcare, and biotechnology. The program emphasizes teaching, mentorship, and the application of rigorous methods to analyze health data.
Program Outline
Degree Overview:
The Biostatistics, Ph.D. program at the University of South Carolina's Arnold School of Public Health prepares students to teach and conduct independent or collaborative biostatistical research across various industries. The program focuses on investigating health conditions and analyzing health programs. It trains researchers to develop and apply biostatistical principles and methods to public health problems. The program is designed for students seeking careers in teaching and research.
Teaching:
The program is part of the Department of Epidemiology and Biostatistics within the Arnold School of Public Health. Students will master advanced biostatistical techniques and learn to teach biostatistics in a classroom setting.
Careers:
Potential career paths include:
- Faculty
- Professor
- Researcher
- Biostatistician
- Biotechnologist Job titles of alumni include:
- Associate Director Biostatistics
- Biomarker Statistician
- Quantitative Analytics Consultant
- Research Statistician Workplace settings include:
- Education
- Biotechnology
- Health Care
- Government
- Insurance
- Life Sciences Employers of alumni include:
- Pfizer
- Novartis
- Bayer
- Takeda
Other:
The program highlights the following:
- Meaningful Work: Integrating mathematics, statistics, and public health to turn data into answers to health-related questions.
- High Demand: Preparing students for work in private industry, universities, research institutes, state health departments, and governmental agencies.
- Dedicated Faculty: Learning from 11 full-time faculty members experienced in using rigorous methods to analyze complex data.
