UCLA Master of Data Science in Health: Curriculum, Requirements, and Opportunities

The UCLA Master of Data Science in Health (MDSH) program is designed to provide professionals with advanced training in data management, data analytics, statistical modeling, machine learning, artificial intelligence (AI), and big data computing. This program caters to individuals seeking to enhance their data science skills for application in hospitals, the pharmaceutical and biotechnological industry, insurance companies, government agencies, and other healthcare and public health administration professional organizations. The MDSH program aims to meet the growing demand for skilled data scientists in the health sector and prepare students to be leaders in this dynamic field. Applications for Fall 2026 enrollment are now open.

The Growing Need for Health Data Scientists

The health sciences are experiencing an explosion of interest in data-intensive research and analytic tools, driven by rapid advancements in high-performance computing, statistical methods, and new programming environments. Technological advancements have led to massive databases containing a variety of health outcomes, accessible to health science administrators, researchers, and policymakers. This "BIG DATA" presents new challenges in training the next generation of data scientists who can analyze these massive databases within the broader health sciences.

In this era of big data technologies, electronically stored digital databases hold information on a variety of health-related issues. Statistical modeling and analysis of such data provide invaluable insights into the various processes and attributes of this data, with the potential to generate new scientific discoveries, shape new health policies, and develop new practices to improve the quality of life. As these databases become increasingly accessible to data scientists, the demand for individuals with the skills to harness emerging technologies and statistical methods to tap into these treasure troves is soaring in hospitals, universities, research organizations, and the pharmaceutical and biotechnology industries, among others.

Program Overview and Objectives

The UCLA Fielding School of Public Health launched the Master of Data Science in Health (MDSH) degree program to help meet this growing demand. This program is tailored to meet the demands of the dynamic and complex health industry by preparing students to be leaders in the field of health data science. The program is designed to appeal to current working professionals seeking to obtain the skills to thrive in a data-rich environment, as well as recent college graduates looking to build a career in the burgeoning field.

The curriculum primarily focuses on developing practical problem-solving skills needed for those eyeing a career in data science within the health sector. Each course incorporates massive data sets that course participants will work with as they develop skills in data engineering, data visualization, mining and exploring data, machine learning methods, and statistical design of research studies. The MDSH degree requires a capstone project through which students apply the data science tools they have learned to explore and solve contemporary problems in the broader health sciences and public health.

Health data science is a growing field that incorporates health informatics, data science, analytics, and computational modeling to assess large volumes of data from clinical trials, electronic medical records, genetic and genomic epidemiology and environmental health, or health care claims. The analysis of these datasets can help solve problems in public health or biomedical sciences.

Curriculum Structure

The MDSH program is a two-year, 48-unit program, consisting of four components:

Public Health Foundation (4 units)
MDSH Core Courses (24 units)
MDSH Elective Courses (16 units)
MDSH Capstone (4 units)

Public Health Foundation (4 Units)

The Public Health Foundation component consists of one required course:

PUBHLT C201: Fundamentals of Public Health: This course explores the foundations of public health by examining public health challenges at local, national, and global levels, and current strategies for advancing population health. It includes the analysis of current public health issues and modern public health policies and practices. If you got a degree from a CEPH-accredited School of Public Health, the Public Heath C201 course can be waived. You have to take an extra MDSH elective to satisfy the 48-unit requirement of the MDSH program.

MDSH Core Courses (24 Units)

The MDSH core courses provide students with a strong foundation in data science principles and techniques. These courses include:

BIOSTAT 203A, B, C: Introduction to Data Science: This 3-course sequence introduces practical data science (data ingestion, data cleaning, data wrangling, data visualization and reporting, databases) and big data computing (parallel, distributed, cluster and cloud computing) skills using computer languages R, Python, SAS, and SQL. Topics include disease cohort characterization, patient-level prediction and population-level estimation using administrative claims and electronic health records. Lectures will cover an introduction of observational health databases, a common data model for representing patient trajectories through healthcare systems, tools to manipulate data while preserving patient privacy theory of patient-level prediction and casual inference from observational data, and best practices for generating reproducible and reliable observational studies. Introductory theory will demonstrate how linear and generalized linear modeling is used in observational studies. Weekly practical laboratories will demonstrate the methods discussed in lecture. Laboratories will use SQL and R software, and regular homework assignments will re-enforce theoretical work with practical application using large-scale synthetic and real-world example databases. Students will design and complete a data analysis project that reflects the best practices covered in this course and translate their results into an oral presentation and written report.
BIOSTAT M215: Survival Analysis: This course covers data science methods for survival and lifetime data.
BIOSTAT 231: Statistical Power and Sample Size Methods for Health Research: This course focuses on sample size and power analysis methods for common study designs, including comparisons of means and proportions, ANOVA, time-to-event data, group sequential trials, linear regression, cluster randomized trials and multilevel data, with emphasis on designing randomized trials. Discussion also of multiple endpoints.
BIOSTAT M234: Applied Bayesian Inference: This course covers the Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods.
BIOSTAT M236: Longitudinal Data: Analysis of continuous responses for which multivariate normal model may be assumed. Students learn how to think about longitudinal data, plot data, and how to specify mean and variance of longitudinal response. Advanced topics include introductions to clustered, multivariate, and discrete longitudinal data.
BIOSTAT 285 Advanced Topics. Machine Learning: Healthcare, Economics, and LLM This course provides an introduction to modern methods in health data science, focusing on the intersection of machine learning, game theory, and economic principles, particularly in the context of decision-making and interactions between multiple self-interested participants. Topics include advanced patient-level prediction, population-level estimation, and the application of large language models (LLMs) to healthcare data. Homework assignments will focus on applying theoretical concepts to practical scenarios. By the end of the course, students will design and complete a data analysis project that incorporates best practices in machine learning and healthcare, translating their results into both an oral presentation and a written report.
BIOSTAT 285 Advanced Topics. Deep learning: a statistical perspective The goal of this course is to study deep learning methodologies and identify related statistical issues. The content includes selected topics from the following: pre-deep-learning methods such as feature extraction and discrimination; components of well-established machine learning tools (support vector machines, reproducing kernel Hilbert spaces, model complexity, sparse models); history of neural networks; multi-layer-perceptron; backpropagation; convolutional neural networks; transformer networks; variational inference; generative adversarial networks; optimization and regularization; visualization; Python and deep learning frameworks.

MDSH Elective Courses (16 Units)

Students must take 16 units from the following electives: Biostatistics 215, 217, 218, 231, M234, M236, and 410.

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MDSH Capstone (4 Units)

BIOSTAT 401: Data Science Capstone: A capstone project that consists of an original written analysis and an oral presentation that addresses an applied health-related data science topic and advances existing skills and techniques in healthcare or public health. Communication skills for professionals. Data ethics training. The capstone project must consist of an original written analysis and an oral presentation that addresses an applied health related data science topic and advances existing skills and techniques in healthcare or public health.

Admission Requirements

Applicants to the Master of Data Science in Health (MDSH) program are expected to fulfill the minimum requirements for admission to the Fielding School of Public Health.

Specific admission requirements include:

Resume/CV: Full-time work experience is not required.
Official Transcripts: A bachelorâs degree with at least a 3.0 cumulative GPA, or the equivalent. You may apply to the program using Unofficial Transcripts. Official transcripts will be required before enrollment. Applicants should use these statements to highlight their academic background, research, or work experience, and their qualifications to contribute to the MDSH and to the field. Include your career goals and how the MDSH might aid in your professional growth.
Three Letters of Recommendation: Uploaded to the UCLA application site directly by your recommenders. Letters should be from a professional and/or academic source.
TOEFL/IELTS Scores: Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) is required for applicants who completed their post-secondary education outside of an Anglophone country. See University Policies and Requirements for details. Be sure to use exactly the same personal information (name, date of birth, gender) in your UCLA Graduate Application as you did in your TOEFL/IELTS testing.
GRE Scores: GRE Scores are optional.

International Students: Visas and Admissions

Visa Type: Graduate students taking at least 8 credit hours, equivalent of 2 MDSH courses, in Fall, Winter, and Spring quarters are considered full-time for F1 visa reporting purposes.
TOEFL/IELTS Exemption: Starting with applications for the fall 2026 admission, if you have earned a bachelorâs degree or higher from an accredited university located in the United States or in another country where English is the sole language of instruction according to the World Higher Education Database (WHED), you are exempt from submitting English proficiency test scores to meet the English language proficiency requirement for admission to UCLA.
Submitting TOEFL/IELTS Score: Official TOEFL score reports are sent electronically upon your request from ETS to UCLA (institution code 4837, department code 99). Official scores are required for admission but not for application. The Admissions Team will then download and attach your official score report.
OPT Eligibility: Yes. Students also qualify for the STEM OPT extension.
Entry Requirements: International students must meet the same requirements as domestic students. In addition, you may be required to certify proficiency in English.
TOEFL Score Requirement: If English is not your first language, and you do not hold a bachelorâs or higher degree from an Anglophone country, you will need to certify your proficiency in English by receiving the minimum overall band score or above on TOEFL or IELTS. This is a mandatory UCLA policy.
TOEFL Home Edition: For applicants for Fall 2023 enrollment, yes. For subsequent application cycles, TOEFL iBT Home Edition and IELTS Indicator test scores received prior to June 10, 2022 can count towards UCLA minimum admission requirements for graduate degree programs. Test scores for the TOEFL iBT or IELTS Indicator exam received beyond this date cannot be considered as alternatives to the TOEFL or IELTS Academic required to meet current English language requirements.
Required Documents from Undergraduate Institution: The documents required depends on your home country. Please reach out to us if you are unsure what documents you need to provide in your application. For applicants graduating from Universities in China, the following documents are required: Transcript, Degree Certificate, and Diploma. All three documents must be provided in both Chinese and English. In your application, you may upload unofficial copies of these documents.
Degree Verification with CSCSE: Yes, we have had alumni successfully verify their MDSH degree with CSCSE. However, international students must maintain full-time student status during the period of study. for an extended period during regular academic quarters may have difficulty verifying their degree with CSCSE.

Application Process and Deadlines

The following deadlines apply to applications for Fall 2026 enrollment:

Priority deadline: February 1, 2026
Regular deadline: April 1, 2026
Final deadline: June 15, 2026

Applications are reviewed on a rolling basis; decisions will be released in the 4-6 weeks following each deadline (priority, regular, and final deadlines). The length of time it will take to release the admissions decision will depend on several factors, including the completeness of the application (transcripts, TOEFL/IELTS scores, Letter of Recommendation, etc.). To ensure the fastest possible processing of your application, be sure to upload all required documents.

Application Fee

The application fee is devoted to the administrative cost of processing applications, and is non-refundable. Citizens or Permanent Residents, or $155 for other applicants.

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Admissions Appeals

Multiple reviewers thoroughly review all applications. The MDSH program does not consider appeals except in light of new information. New information is limited to academic qualification which may include higher GPA, grades, and certification not already included in the application. TOFEL/IELTS scores are not considered new information. Due to the volume of applications, we do not provide individual feedback on the quality of applicants nor provide suggestions on how to strengthen future applications.

Academics and Student Life

Mixed Enrollment: Taking two UCLA degree programs at the same time is called mixed enrollment. Because MDSH is a new degree program, it is not yet set up for this. We hope to allow mixed enrollment with MDSH in future years.
Taking Non-MDSH Courses: MDSH students can take non-MDSH courses at UCLA. However, non-MDSH courses do not count towards the 48-unit requirement for the MDSH program and students will be charged extra tuition.
MPH Degree from UCLA: The Public Heath C201 course can be waived if you got a degree from a CEPH-accredited School of Public Health. You have to take an extra MDSH elective to satisfy the 48-unit requirement of the MDSH program.
Transcript and Degree Information: No. Neither the transcript nor Degree will mention the hybrid course mode. The MDSH program is considered a full-time program for F-1 Visa purposes, however, domestic students are welcome to take the program part-time. Transcripts will reflect each studentâs actual course enrollment per quarter.
University Dormitories: Yes, although there may be a waiting list. Please contact the UCLA Housing for more information.
Computer Requirements: For the MDSH coursework, computers with a minimum of 32GB RAM and 1 TB hard disk suffice.

Additional Information

Advising: An adviser is appointed for each new masterâs student by the MDSH Program Director. An adviser is a teaching faculty in the MDSH program and is responsible for the studentâs academic progress. Progress is evaluated on an ongoing basis. At the end of each quarter, the Associate Dean of Student Affairs reviews academic listings of students and notifies them and the advisers when the cumulative grade-point average is below 3.0. Advisers review each case with their advisees and make recommendations to the Division of Graduate Education for academic continuance or disqualification.
Course Requirements: The MDSH degree program requires 12 4-unit courses, including one capstone project. It takes a minimum of 20 months (6 academic quarters) to complete. Required courses are Public Health C201, Biostatistics 203A, 203B, 203C, 100A, 212A, and 212B. Students must take 16 units from the following electives: Biostatistics 215, 217, 218, 231, M234, M236, and 410. Only courses in which a grade of C or better is received may be applied toward the requirements for a masterâs degree. Courses taken for S/U grading may not be applied toward the degree requirements.
Normative Time: From graduate admission to award of the degree, normative time is 6 academic quarters (20 months). 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.

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