Applicable only to students admitted during the 2024-2025 academic year.

Computational Medicine

School of Medicine

Graduate Degrees

The Department of Computational Medicine offers the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Biomathematics, the Master of Science (M.S.) degree in Clinical Research, and the Master of Science (M.S.) degree in Data Science in Biomedicine.

Biomathematics

Admissions Requirements

Master’s Degree

Advising

The faculty director of the Data Science in Biomedicine M.S. (DSB) program is in charge of student advising and the overall academic management of the program. Students may arrange to meet with the faculty director to discuss any academic or personal concerns that arise during their studies. In the case of academic performance concerns, students may be asked to meet with the faculty director to discuss remediation.

Areas of Study

Data Science in Biomedicine

Foreign Language Requirement

None.

Course Requirements

Data Science in Biomedicine M.S. students must complete at least 9 courses and earn 36 units of 200-level course credit including five core courses (20-24 units). One course must be a capstone course (4-8 units), which includes a major project. In addition, students take additional electives of 200-level courses from the Data Science in Biomedicine M.S. course offerings, or courses from the Engineering MS Online program, data science focus area to reach the 36 unit and number of courses requirements.

Core Courses

The core consists of the following courses. Each course is four units, with the exception of DSB 220, which can be 4 to 8 units. (Students with a more substantial capstone project can take DSB 220 for 8 units.) Courses marked with an asterisk are capstone courses.

DSB 200 Foundations of Data Science
DSB 205 Machine Learning Applications in Biomedicine
DSB 206 Advanced Machine Learning Applications in Biomedicine
DSB 207 Data Science for Medical Imaging
DSB 218* Applied Data Science in Genomics and Biomedicine
DSB 219* Data Science Algorithms in Biomedicine
DSB 220* Data Science in Biomedicine Supervised Project

DSB 200 is offered in the fall quarter only. It is required as the first course in the program, unless the student can demonstrate comparable knowledge of the curriculum and qualify for a waiver. If DSB 200 is waived, the student must complete an additional elective course to satisfy the 36 unit requirement.

Elective Courses

In addition to core courses, students take four elective courses. Elective options include additional DSB 200-level courses including:

DSB 208 Recent Research in Machine Learning in Medicine
DSB 209 Recent Research in Data Science in Genomic Medicine

Students may take an additional core course as an elective, and may also take courses in the Engineering MS Online Program, Data Science focus area, as electives, including:

COM SCI 249 Current Topics in Data Structures
EC ENGR 205A Matrix Analysis for Scientists and Engineers
COM SCI 249 Big Data Analytics
EC ENGR 219 Large-Scale Data Mining: Models and Algorithms
COM SCI 260 Machine Learning Algorithms
EC ENGR 232E Large-Scale Social and Complex Networks: Design and Algorithms
COM SCI 262A Learning and Reasoning with Bayesian Networks
EC ENGR M214A Digital Speech Processing
EC ENGR 214B Advanced Topics in Speech Processing
COM SCI 264A Automated Reasoning: Theory and Applications

Teaching Experience

Not required. However, we expect students to be employed full-time in the biotech/pharma industry while enrolled in the master’s program.

Field Experience

Not required.

Capstone Plan

The capstone plan requirement is fulfilled by successful completion of one capstone course, DSB 218, 219, or 220 with a grade of “B” or better. Students complete a project that is designed to provide an in-depth exposure to at least one major task they will be expected to fulfill in the workplace. The project must develop data science methods and techniques and apply them to a problem in medicine. The faculty member teaching the course will supervise the project to ensure that the students’ work and contribution adhere to the rigorous academic requirements of the program. Evaluation consists of a combination of a written and oral presentation based on individual and team effort if applicable.

Thesis Plan

None

Time-to-Degree

The program is designed to be part-time, and most students will take no more than one course per quarter and will complete the program in 9 quarters. We expect half of the students to take a course in the summer quarter, and half to only take courses in the fall, winter and spring quarters. The student must complete the degree within four years plus one quarter.

DEGREE	NORMATIVE TIME TO ATC (Quarters)	NORMATIVE TTD	MAXIMUM TTD
M.S. in Data Science in Biomedicine	9-12	9-12	17

Academic Disqualification and Appeal of Disqualification

University Policy

A student who fails to meet the above requirements may be recommended for academic disqualification from graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing academic disqualification of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA.