No. The program has been specifically designed to be taken in sequence to be completed
over three or four semesters. Due to the amount of credits and requirements for the
degree, finishing in two semesters is not possible.
The program is more career-oriented and is intended to be a terminal degree that prepares
students for a career in a related field. However, the skill set learned is also beneficial
for a research-oriented career or future degree.
Proficiency in a computer programming language, such as R. or Python, is helpful for
eligibility of admission. The best way to prepare yourself is to take mathematics,
statistics, and computing courses at academic institutions. You may also complement
your skill set by taking additional courses online, such as EdX or Coursera.
Industry training is not a requirement for admission but does show interest and experience
in the field. Industry training is also not a requirement for the program but may
be helpful during the program. Academic training is beneficial before starting the
We provide interdisciplinary training in statistics, computing and health science.
Specifically, our program has a strong focus on integrating statistical inference
and big data computing with solving important problems in health care. We emphasize
the importance of statistical inference and scalable computational tools, and students
gain a firm foundation in health science in our curriculum. Students are trained to
become interdisciplinary quantitative leaders in analyzing and interpreting massive
and complex data in health sciences.
Health data refers to any data that pertains to the biomedical sciences and public
health. Data sets might originate from observational studies, clinical trials, computational
biology, electronic medical records, health care claims, genetic and genomic epidemiology
and environmental health, network health science, and many other fields.
Yes. Students can cross-register for courses at all Saint Louis University schools
and colleges. However, only graduate-level courses may be taken. Please note that
additional courses will not count toward credit for the degree requirements.
This is a project-based research course that will allow students to gain practical
skills in analyzing and interpreting different types of big data in health care. Each
student will work with an organization of their choice and preceptor from the organization.
In addition, a faculty member from the department will oversee the capstone course
Our graduates are employed as data scientists, data managers, data analysts, machine
learning engineers, statisticians, software engineers, and quantitative analysts in
academia, government and industry. It is also possible for students to further their
education in a doctoral program in a related field.
Absolutely. While many of the examples and problems in the course curriculum will
center around current topics in health care, the statistical and computational skill
set and tools obtained are broadly applicable to many areas of data science.
O.P.T. is one type of work permission available to certain F-1 nonimmigrant students.
It allows students (except those in English language-training programs) to obtain
real-world work experience directly related to their field of study.
The STEM O.P.T. extension is a 24-month extension of O.P.T. available to F.-1 non-immigrant
students who have completed 12 months of O.P.T. and received a degree in an approved
STEM field of study as designated by the STEM list.