Mastering Data Science at UCLA: A Comprehensive Guide to the MASDS Program

The field of data science is experiencing unprecedented growth, driven by the increasing volume and complexity of data across various industries. Professionals who can extract meaningful insights from this data are in high demand. UCLA answers this call with its Master of Applied Statistics and Data Science (MASDS) program. This guide will provide a comprehensive overview of the MASDS program, including its curriculum, admission requirements, and career prospects.

The Growing Demand for Data Scientists

The art of data science has been around since the beginning of human existence. People make observations about their surroundings, create a model and make predictions to manage uncertainty. As technology has advanced, so has the amount of data that can be mined for insights and optimizations, and with that, the tools needed to procure, visualize, analyze and predict have also revolutionized. Data science job growth has been much faster than the average profession in the nation. The Big Data market size is projected to grow from $162.6 billion in 2021 to $273.4 billion in 2026 and accounted for 51% of the big data market spending in 2021. In every industry, there is a significant need for professionals who understand how to achieve efficiency and discover ways to optimize productivity and improve user experience for consumers.

UCLA's Master of Applied Statistics and Data Science (MASDS) Program

The MASDS program at UCLA is designed to equip students with the knowledge and skills necessary to thrive in this data-driven world. The curriculum blends the fundamental ideas of quantitative analysis with a modern computational science approach. Graduates of the program will have mastered the principles of statistics and be ready to apply them in a broad range of areas. This balance will make graduates highly valuable to employers needing quantitative or data science skills. The focus of the MASDS program is applied statistics and data science. Most classes will be scheduled in the evening to make it easier for students who are concurrently working professionals. Students in the MASDS program may choose to take 1 or more courses as long as they complete the program within 36 months.

Curriculum Overview

MASDS students must earn a minimum of 44 units of course credit. MASDS students will enroll in the 400 level courses. The Master of Applied Statistics & Data Science program has a set of seven required core courses. In addition, students will choose at least four electives that emphasize statistical modeling and programming. Note: MASDS students may choose to take up to two courses per quarter, including the summer, to complete their total of 11 courses. The MASDS program is classified differently from other graduate programs at UCLA. Students must complete 44 units to complete the program. A typical course in the program is 4 units. Students in the MASDS program may choose to take 1 or more courses as long as they complete the program within 36 months. A typical load for MASDS students is 8 units (2 classes) per quarter.

Core Courses

The core consists of seven courses in statistical theory and methods. Among the core courses, only 401 may be waived upon departmental approval. Among the core courses, a course may be waived by request if the Director determines the student has already completed equivalent coursework. A maximum of 3 core courses may be waived. The Director with consultation from the MASDS governance committee will review student transcripts, syllabi, and other relevant materials to evaluate whether or not the student has already learned the bulk of the material taught in the courses. For a waiver to be considered, the prior coursework taken would need to be equivalent to the graduate level.

Elective Courses

In addition to the seven core courses, students must complete four courses (16 units) of their choosing as electives. For students entering in Fall 2025 or later electives must be Statistics 400-level courses or the 200 level courses specified in restrictions. 498 Master’s Thesis Research courses may not count as an elective. Only 4 units of Stats 497 (Individual Studies) may count towards the required electives. 4 units of Stats 496 (Statistics Internship) may also count towards the required electives upon approval of the M.A.S. MASDS students may only take courses with a course number between 203 and 283. MASDS students who are taking or have taken a similar 400 level class cannot take the replacement 200 level class; such similar courses are listed below. If the MASDS student takes the 200 level class, then it replaces the corresponding 400 level class. For these 200 level courses, MASDS students will have lower enrollment priority than PhD and MS students.

Thesis Requirement

All students will complete a thesis, which will report on research and analysis done under the supervision of both a faculty member and an industry partner when available. While students may work with proprietary data, industry partners must agree that the student can publish and distribute the thesis. The thesis must consist of an original analysis that solves a real-world problem. A faculty adviser of the Department of Statistics & Data Science will supervise the thesis project, who will ensure the statistical integrity of the analysis. Most thesis topics will originate from industry partners, who will propose topics and provide data sets. For assistance in choosing a faculty advisor, please click on this link to review our faculty research. Once you have selected your faculty advisor, please submit this form to your faculty advisor.

Academic Advising and Mentorship

The Director of the Master of Applied Statistics program is a regular Statistics faculty member who heads a committee of faculty members who may serve as academic advisers. The research interests of the members of this committee span most of the major areas of statistics. Each student chooses a primary consulting academic adviser who is responsible for monitoring the student’s degree progress and approving the study list each quarter. All students work with their primary consulting academic adviser in the first quarter of their second year to adopt a plan for degree completion. Advising and mentoring is done by the primary consulting adviser, who may either serve as a master’s committee member or also chair the student’s master’s thesis committee. Students meet with their primary thesis adviser monthly until the degree is completed, to ensure that students are assigned to and working on a thesis project that allows for timely completion of the degree.

Program Timeline and Disqualification

Students must complete the requirements for the Master of Applied Statistics, including the written thesis, within 10 academic quarters. 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. A student who does not complete all the requirements for the M.A.S. degree within 10 quarters is subject to a recommendation for academic disqualification. The graduate vice chair decides in each case whether a recommendation for academic disqualification is warranted.

Admission Requirements for the MASDS Program

Strong applicants to the MASDS program will have a strong quantitative background including: linear algebra, multivariate calculus, probability and statistics and programming/coding in some language. Applicants must be adept at using advanced quantitative concepts in order to complete the program.

Academic Background and Experience

Important degree preparation is likely to come from job experience, in addition to undergraduate education. Many applicants will have gained valuable work experience in fields that have, or need, quantitative thinking. We will highly value documented work experience in applying quantitative methods in research, business, or other professional environments. applicant is a bachelor’s degree from a regionally accredited institution, comparable in standard and content to a bachelor’s degree from the University of California.

Application Procedure

For application purposes only, students will need to upload a copy of their transcripts and exam scores on to the online application which will be used to evaluate your admission’s decision. All applicants must apply online via UCLA’s Application for Graduate Admission. You will be asked for “Major Code” when you apply. It is 00J3.

Standardized Tests

When you submit a GRE score, an official GRE General Test score must be sent directly by the Educational Testing Service (ETS). The GRE Code for UCLA is 4837 and the code for the Statistics & Data Science department is 0705. There are no substitutes for the GRE, including the GMAT. A GRE score is valid if the examination was given no more than five years prior to the date your application is submitted. *The GRE requirement may be waived for applicants who have highly competitive holistic portfolios and such requests will be reviewed on a case-by-case basis.

Required Documents

Please note that submitted records become the property of the University and cannot be returned. If you are a university/college senior, do not risk missing the deadline by waiting for senior-year grades to be posted before submitting your application and transcript. Applicants are required to submit a statement of purpose and personal statement. These statements provide an opportunity for applicants to demonstrate to the Admissions Committee how their previous academic training, research or interest areas, work experience, and any other relevant evidence based qualifications have prepared them to contribute to the UCLA MASDS program and to the field. Both statements will also assist the committee in determining if a fellowship can be awarded. Applicants must submit a resume or CV. Two letters of recommendation are required from individuals who are well acquainted with your performance in academic or work settings, preferably direct professional or academic supervisors.

Fees and Financial Aid

citizens and Permanent Residents and $155.00 for all other applicants. The application fee is devoted to the administrative cost of processing all applications received, and is not refundable under any circumstances, regardless of the outcome, the date of filing, time of review, or if, for whatever the reason, the application is withdrawn. If a student chooses a typical path of 2 courses per quarter, this amounts to $27,038 per year. Students enrolled in this program are also required to pay student services fee, campus‐based fees and health insurance. **Application fee waivers are available for applicants who are current or former participants in certain programs or who demonstrate financial need. The Department of Statistics intends to make the program accessible to all prospective students. The MASDS program offers fellowships to assist a few students with paying the program costs.

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Housing

The Department of Statistics & Data Science does not provide housing assistance. UCLA Housing has on campus living options and provides assistance in locating off campus housing.

Career Opportunities for MASDS Graduates

The UCLA MS in Data Science program provides graduates with excellent career prospects across a variety of sectors. Thanks to the program’s strong focus on practical data science skills, graduates are prepared to enter fields like technology, finance, healthcare, consulting, and government.

Specific Roles

In the technology sector, graduates often find roles such as Data Scientist, Machine Learning Engineer, Data Analyst, and Business Intelligence Analyst.
Finance offers roles like Quantitative Analyst, Financial Data Analyst, Risk Analyst, and Actuarial Analyst.
For those entering the healthcare sector, job roles include Healthcare Data Analyst, Clinical Data Scientist, Biostatistician, and Epidemiologist.
Consulting provides opportunities as an Analytics, Data Science, or Strategy Consultant.
In government and non-profit sectors, typical job roles include Data Analyst, Policy Analyst, Research Analyst, and Program Evaluation Specialist.

Additional Data Science Programs at UCLA

Master of Data Science in Health (MDSH)

The UCLA Master of Data Science in Health (MDSH) Program provides advanced training in data management, data analytics, statistical modeling, machine learning, artificial intelligence (AI), and big data computing for professionals who seek enhanced data science skills for hospitals, pharmaceutical and biotechnological industry, insurance companies, government agencies, and other healthcare and public health administration professional organizations. This program is designed to appeal to current working professionals seeking to obtain the skills to thrive in a data-rich environment as well as to recent college graduates looking to build a career in the burgeoning field. The curriculum primarily focuses on developing practical problem-solving skills that are needed for those eyeing a career in data science within the health sector.

Online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR)

The UCLA Samueli School of Engineering offers an online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR) that prepares students to solve problems and harness data ethically using responsible artificial intelligence through a curriculum of five core courses and four electives. This is a part-time, fully online degree program intended for working professionals. Classes can be accessed asynchronously from anywhere in the world. However, students are required to meet regular deadlines for submitting assignments, and exams are proctored in person at their local testing centers. Our online M.S. in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR) provides engineers and scientists the opportunity to explore and interact with popular data science tools including deep-learning libraries like PyTorch and TensorFlow, advanced probabilistic-reasoning tools such as Bayesian Inference and distributed-computation systems like MapReduce. MSOL: DATA SCIENCE ENGR students have the opportunity to build an inclusive network. Online students can also join affinity organizations such as the National Society of Black Engineers, Queer and Trans in STEM, the Society of Latinx Engineers and Scientists and the Society of Women Engineers. Designed for working professionals in science, technology, engineering and mathematics (STEM), and in particular those with an interest in the intersection of engineering and data science.

Data Science in Biomedicine MS

The Data Science in Biomedicine MS provides foundational training in all areas of data sciences including machine learning, statistics, data mining and analytics in combination with training in the analysis of biomedical data including electronic health records, medical images and genomic data. A minimum of 36 units (the equivalent of nine courses) are required to complete the degree. The degree will require 5 core courses, including a capstone course, plus 4 electives. Each core and elective course is four units, with one possible exception: a student may take a capstone course for up to 8 units.

Course Examples

Here are some examples of courses offered, showcasing the breadth of topics covered:

COM SCI 241A: Data Mining: This course covers basic data mining algorithms, advanced topics on text mining, recommender systems, and graph/network mining. A team-based project involving hands-on practice of mining useful knowledge from large data sets is required.
COM SCI 260C: Deep Learning: This course teaches the basics of deep neural networks and their applications, including computer vision, natural language processing, and graph mining. The course covers topics including the foundation of deep learning, how to train a neural network (optimization), architecture designs for various tasks, and some other advanced topics.
EC ENGR 236A: Information Theory: This course introduces students to the research developments and new mathematical techniques for emerging large-scale, ultra-reliable, fast and affordable data storage systems.
STATS 232A: Statistical Modeling and Data Analysis: Students are introduced to a variety of scalable data modeling tools, both predictive and causal from different disciplines. Topics include supervised and unsupervised data modeling tools from machine learning, such as support vector machines, different regression engines, different types of regularization and kernel techniques, deep learning and Bayesian graphical models.

Preparing for the MASDS Program: Community College Pathways

Full sequences can take two to four terms to complete (depending on whether your institution is on semesters or quarters), so you'll want to start them early. Use ASSIST to find the specific classes offered at your community college that will satisfy the expected coursework at a particular UC campus. The Data Science Pathway applies to the degree programs listed below.

Data science, B.A.
Data science, B.S.
Data science, B.S.
Data theory, B.S.
Data science and analytics, B.A.
Data science and computing, B.S.
Data science, B.S.
Data science, B.S.
Statistics and data science, B.S.

Admission to different UC campuses and majors varies in competitiveness depending on how many students apply and how many slots are available. As a result, the minimum GPA and grade requirements for particular courses may vary from campus to campus.

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