UCLA Data Science Programs: A Comprehensive Overview

The University of California, Los Angeles (UCLA) offers a variety of data science programs designed to equip students with the skills and knowledge necessary to thrive in the rapidly evolving field of data science and analytics. These programs cater to a diverse range of students, from undergraduates seeking a strong foundation to working professionals aiming to enhance their expertise.

Master of Applied Statistics & Data Science (MASDS) Program

The Master of Applied Statistics (MAS) program was created in the Fall of 2016 in response to the increasingly high demand from students seeking a master’s degree in data science and quantitative analytics. Recognizing the growing importance of data science, the program evolved from MAS to Master of Applied Statistics & Data Science (MASDS) in Spring 2023 to accurately reflect the program and department’s mission to develop data scientists. As of Fall 2025, UCLA welcomed the tenth cohort to the MASDS program and is pleased to congratulate the 8th graduating class.

The UCLA MASDS Program offers all of its courses in person in the evenings, in order to accomodate working professionals to complete the degree while working. The program is perfect for those who wish to transition to a more quantitatively focused career. All classes are taught by UCLA faculty who are experts in their areas of instruction.

Curriculum and Focus

Most Data Science programs focus on teaching students the methods of data modeling, analysis, and engineering. What is missing is a rigorous understanding of the statistical and mathematical foundational concepts that underlie these methods. Without these, data scientists lack the understanding to deal with the plethora of problems they will face. Designed and taught by UCLA’s world-class statistics faculty, the program blends the fundamental ideas of quantitative analysis with a modern computational science approach. Graduates of the program will have mastered the principles of data science and AI 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.

Affordability and Value

The UCLA MASDS program is significantly more affordable than competing programs while delivering exceptional value. As of 2026, tuition is just $27,038 per year for two years for a typical student taking two courses per quarter. This represents outstanding value, particularly given that MASDS has the highest job placement rate among all UCLA master’s programs and offers fully in-person instruction from world-leading experts in data science.

Data Theory Major (Undergraduate)

The Data Theory major at UCLA joins the strength of UCLA’s Mathematics department with the innovation of its Statistics department to offer undergraduate students a world-class education in the foundations of Data Science. This capstone major is the first in the world, both in name and content.

Key Academic Differences

One key academic difference from the Data Science majors proposed by peer universities is the presence in our major of substantial upper division proof-based mathematics. The major is strong, perhaps stronger than some Masters degrees, in Machine Learning, which is at the core of Data Science.

Master of Data Science in Health (MDSH) Program

The UCLA Fielding School of Public Health has launched the Master of Data Science in Health (MDSH) degree program to help meet this growing demand. 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.

Program Structure and Focus

Complete your degree in 20 months by attending in-person classes one weekend per month and online evening sessions! Applications for Fall 2026 enrollment are now open! 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 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. Each course will incorporate massive data sets that course participants will work with as they develop skills in data engineering, data visualization, mining and exploring of data, machine learning methods, and statistical design of research studies. The MDSH degree will require 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.

Message from the Senior Associate Dean

“Data-intensive research and the development of analytic tools in the health sciences continue to witness an explosion of interest largely attributable to rapid developments in high-performance computing, statistical methods and new programming environments. Technological advances have produced massive databases on a variety of health outcomes that are accessible to health science administrators, researchers and policy-makers. This data deluge (“BIG DATA”) poses new challenges in training the next generation of data scientists who will be tasked with analyzing massive databases in the broader health sciences. In this era of big data technologies, massive electronically stored digital databases carry information on a variety of health-related issues. Statistical modeling and analysis of such data will provide invaluable insights into the various processes and attributes of such data with the potential to generate new scientific discoveries, shape new health policies and develop new practices to improve our 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.

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

Our online M.S. in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR) is unique in its interdisciplinary approach. Explore prominent data science tools and topics while gaining the critical knowledge needed to drive actionable insights grounded in science. Through well-tested coursework and hands-on experience with data exploration and visualizations, learn how to use data to solve real-world problems, optimize efficiency across industries and engineer positive change that improves the lives of people around the globe.

Program Structure and Requirements

Nine courses are required (36 units). A minimum of five courses must be taken at the graduate level (excluding ENGR 299 Capstone Project course). This program includes a Comprehensive Exam Requirement that every student must complete to earn their degree. The online MSOL: DATA SCIENCE ENGR is a part-time program. Students who take one course each quarter are able to complete the program over the course of two years and a quarter, including two summer sessions. You may also take more than one course per quarter if desired in order to earn your degree sooner.

Capstone Project

Preparation: completion of a minimum of five 200-level courses in the MSOL: DATA SCIENCE ENGR program. This project course satisfies UCLA’s final comprehensive examination requirement for any MSOL in Engineering degree. The project is completed under individual guidance from a UCLA Engineering faculty member and incorporates advanced knowledge learned in the MSOL: DATA SCIENCE ENGR program of study.

Data Science Course Descriptions

UCLA offers a wide range of courses covering various aspects of data science. Here's a glimpse into some of them:

Introductory Courses

Get an introduction to the foundational concepts and techniques behind data science and its applications. Students will learn about programming languages including Python and R, and receive a primer on natural language processing, big data management and visualization techniques. Students are expected to have basic Python programming and basic statistics skills. Learn to leverage the power of big data to extract insights and improve decision making for real-world problems.

Read also: Mastering Data Science

Advanced Courses

Survey the main topics and latest advances in big data analytics, as well as a wide spectrum of applications such as bioinformatics, E-commerce, environmental study, financial market study, multimedia data processing, network monitoring and social media analysis.

Go through an in-depth examination of a handful of ubiquitous algorithms in machine learning. While this course covers several classical tools in machine learning, it primarily explores recent advances in machine learning, as well as developing efficient and provable algorithms for learning tasks. Topics include low-rank approximations, online learning, multiplicative weights framework, mathematical optimization, outlier-robust algorithms and streaming algorithms.

Machine Learning and Deep Learning

This course introduces students to the problems of identifying patterns in data. Machine learning allows computers to learn potentially complex patterns from data and make decisions based on these patterns. Learn the fundamentals of this discipline and gain both conceptual grounding and practical experience with several learning algorithms. Techniques and examples throughout the course show how machine learning is used in areas such as healthcare, financial systems, commerce and social networking.

In this course, we teach the basics of deep neural networks and their applications, including but not limited to 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.

Specialized Topics

Natural language processing (NLP) enables computers to understand and process human languages. NLP techniques have been widely used in many applications, including machine translation, question answering, machine summarization and information extraction. Study the fundamental elements and recent trends in NLP. Students gain the ability to apply NLP techniques in text-oriented applications, understand machine learning and algorithms used in NLP and propose new approaches to solve NLP problems.

Modeling and design of large-scale complex networks, including social networks, peer-to-peer file-sharing networks, World Wide Web and gene networks. Modeling of characteristic topological features of complex networks, such as power laws and percolation threshold. Introduction to network algorithms, computational complexity and nondeterministic, polynomial-time completeness.

Certificate Programs

UCLA Extension offers several certificate programs related to data science, providing focused training in specific areas.

Geographic Information Systems (GIS) and Geospatial Technology

Learn how to use location-based data to better understand the world. Offered in partnership with the UCLA Department of Geography, this certificate provides an introduction to the methods and techniques used within the field of GIS and geospatial technology.

Blockchain Technology

Stay ahead in this evolving ecosystem and deepen your understanding of blockchain and distributed ledger technology (DLT).

Cybersecurity

Learn essential cybersecurity skills in our 4-course Cybersecurity Certificate. Quickly gain the knowledge you need to protect your technology infrastructure from physical and virtual threats.

Software Development

Develop software applications in Java and Python and enhance your web development skills. Gain the practical knowledge necessary to compete in an evolving technology ecosystem.

Information Systems Analysis and Design

This certificate provides training in analysis and design of information systems. The program prepares students to perform information systems requirements analysis, design, development, installation, and operation as well as testing and documentation. Courses include computer network communication protocol TCP/IP, database management, network security, and operating systems.

Skills Development

Learn the iterative process of exploratory data analysis (EDA), data analysis techniques, data exploration, and visualization. Learn tools for distributed data storage and large data set processing. Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis.

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