Understand & Apply Standard Statistical Techniques
Biostatistics is the science of developing and applying statistical methods needed to tackle research problems in a variety of fields – especially public health and medicine. The Applied Biostatistics Graduate Certificate, offered by the School of Public Health, is for individuals who lack formal training in biostatistics and who would like to gain skills needed to understand and apply standard statistical techniques. The certificate is designed to develop a basic understanding of standard statistical techniques, study design, data modeling and inference. This program is especially useful for clinical and translational researchers, as well as public health practitioners.
Students in the Applied Biostatistics Certificate program typically take one to two classes per semester. Completion of the 15-credit-hour program usually takes two years.
Coursework begins with a traditional introduction to biostatistics in which basic principles of statistical inference and data analysis are introduced, along with a corresponding computing section that demonstrates analytic tools in a practical setting using statistical software. Later courses cover regression principles and more sophisticated data analysis techniques. All of the courses incorporate important epidemiologic concepts, and an introductory course in epidemiology is recommended early in the program.
The final capstone course allows students to utilize the knowledge gained in their coursework to analyze real data of their own in a comprehensive manner. Students work on a dedicated data analysis stemming from their own research or the work of others, culminating in a final research paper. Although there are no formal class meetings, students complete the equivalent of 30 contact hours, plus assignments, by the end of the semester. A formal report or manuscript, depending on the situation of the student, is required at the end of the semester.
The primary objectives of the program are to:
- Describe basic concepts of probability and statistical inference;
- Demonstrate standard techniques of database management and analysis;
- Compare and contrast study designs common to health research;
- Recognize the primary sources of bias observed in health research;
- Interpret appropriate inferences from data, based on strengths and limitations of major epidemiologic study designs, as well as the results of descriptive and inferential statistical analyses.