Master's in Applied Data Sciences Frequently Asked Questions

Applicants must have an undergraduate degree (BA, BS or equivalent) in biology, chemistry, physics, mathematics, statistics, computer science, engineering or a similar scientific discipline; U.S. citizenship or permanent resident status or be sponsored by another institution on a Visa that allows for a full-time course of study in a degree-granting program; and English language proficiency (verbal and written).

After the applicant uploads a Curriculum Vitae, transcripts, essays, and referee contact information, the applicant submits the online application. The applicant’s information then becomes for consideration by the Graduate School.

The admissions committee identifies the top candidates based on past academic achievements, including results of transcripts, how the applicant’s goals and interests match the objectives of the program, and interest in the field of biomedical data science. Applications will identify those that are prepared to succeed in advanced level course work and completion of a major practicum project that advances the St. Jude mission.

There is no fee to submit the application.

The Graduate School does not require applicants to submit GRE or MCAT scores for admission into the master’s program.

Each course will be delivered through an accelerated format where two or three credit hours are earned in three weeks. During the first 10 months, students should expect to attend 12.5 hours of class per week and to spend much of their remaining time dedicated to completing their degree requirements. In their practicum, students will have expectations similar to that of a full-time employee of St. Jude.  Class times will be established and available to potential students in advance. 

The twelve-month practicum is a mentored project that is overseen by a committee of three or more St. Jude faculty members. Each student will have one or two mentors, one of whom must be a data scientist with the other mentor being a clinical or lab scientist. The goal is to explore the pipeline of raw data to research via a project that has a formal proposal and roadmap and concludes in a written thesis (publication), oral presentation and creation of a software or data base resource.