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. These tools and topics include:

  • deep-learning libraries (e.g., PyTorch and TensorFlow)
  • advanced probabilistic-reasoning techniques (e.g., Bayesian inference)
  • distributed-computation systems (e.g., MapReduce)

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.

Degree 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. This ensures that you graduate from the program with an in-depth, practical understanding of data science and how to implement it into your professional life.

Time to 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. The maximum time allowed in this program is three academic years (nine quarters), excluding summer sessions.

Man on computer

Coursework and Format

The online MSOL: DATA SCIENCE ENGR coursework includes:

  • Recorded lectures
  • Discussion posts
  • Written assignments
  • Individual and group projects
  • Exams

Course Descriptions

CORE COURSES IN DATA SCIENCE ENGINEERING

Five core courses are required.

Lecture, four hours; laboratory, two hours; outside study, six hours. In this course, students learn about information systems and database systems in enterprises, file organization and secondary storage structures, relational model and relational database systems, networks, hierarchical and other models, query languages, database design principles, transactions, concurrency and recovery, and integrity and authorization. Letter grading.

COM SCI 245. Lecture, four hours; outside study, eight hours. Recommended requisite: COM SCI 143 or equivalent. With the unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it. Data analytics is the process of automatic discovery of patterns, changes, associations and anomalies in massive databases. It is a highly inter-disciplinary field representing a confluence of several disciplines, including database systems, data warehousing, data mining, machine learning, statistics, algorithms, data visualization and cloud computing. 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. Letter grading.

EC ENGR 219. Lecture, four hours; discussion, one hour; outside study, seven hours. In this course, 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. There is an emphasis on techniques that evaluate relative performance of different methods and their applicability. The course includes computer projects that explore the entire data analysis and modeling cycle: collecting and cleaning large-scale data, deriving predictive and causal models and evaluating the performance of different models. Letter grading.

Lecture, four hours; outside study, eight hours. This course is an introduction to the concepts, algorithms and techniques of data mining on different types of datasets, covering basic data mining algorithms, advanced topics on text mining, recommender systems and graph/network mining. A team-based project involving the hands-on practice of mining useful knowledge from large data sets is required. Letter grading.
Topics may vary by term. Lecture, four hours; outside study, eight hours. Review of current literature in the area of data structures in which the instructor has developed special proficiency as a consequence of research interests. Students report on selected topics. May be repeated for credit with the consent of the instructor. Letter grading.
Lecture, four hours; discussion, two hours; outside study, six hours. 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. Letter grading.
Lecture, four hours; outside study, eight hours. 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. S/U or letter grading.

COM SCI 260C. Lecture with discussions, 4 hours. Recommended requisite: COM SCI 260. 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. By the end of the course, the students are expected to be familiar with deep learning and be able to apply deep learning algorithms to a variety of tasks.

EC ENGR C247. Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisites: EC ENGR 131A or equivalent. Topics of this course include a review of machine learning concepts; maximum likelihood; supervised classification; neural network architectures; backpropagation; regularization for training neural networks; optimization for training neural networks; convolutional neural networks; practical CNN architectures; deep learning libraries in Python; recurrent neural networks, backpropagation through time, long short-term memory and gated recurrent units; variational autoencoders; generative adversarial networks; adversarial examples and training. Concurrently scheduled with course C147. Letter grading.

To alleviate costs and improve robustness and generalization performance of modern machine learning models, it becomes crucial to develop methods with strong theoretical guarantees to warrant efficient, accurate and robust learning. Discussion of advanced topics and state-of-art research to improve efficiency, robustness and scalability of machine learning algorithms on large data. Topics include advanced optimization, variance reduction, distributed training, federated learning, data summarization, robust learning, neural network pruning, neural architecture search, neural network quantization.
Lecture, four hours; outside study, eight hours. Recommended requisite: EC ENGR 131A. Topics include a review of several formalisms for representing and managing uncertainty in reasoning systems and a presentation of the comprehensive description of Bayesian inference using belief networks representation. Letter grading.
Lecture, four hours; outside study, eight hours. 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. Letter grading.
Lecture, four hours; laboratory, four hours; outside study, four hours. This course gives students an introduction to the theory and practice of automated reasoning using propositional and first-order logic. Topics include syntax and semantics of formal logic; algorithms for logical reasoning, including satisfiability and entailment; syntactic and semantic restrictions on knowledge bases; effect of these restrictions on expressiveness, compactness and computational tractability; applications of automated reasoning to diagnosis, planning, design, formal verification and reliability analysis. Letter grading.
Lecture, four hours; recitation, one hour; outside study, seven hours. 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. Mining topology to design algorithms for various applications, such as e-mail spam detection, friendship recommendations, viral popularity and epidemics. Introduction to network algorithms, computational complexity and nondeterministic, polynomial-time completeness. Letter grading.
Yizhou Sun

“Because our program couples engineering knowledge with data science, graduates not only understand the scientific model, but can also use coding techniques to build a real-world system based on data.”

—Yizhou Sun, associate professor of computer science

Recommended Electives for Data Science

As long as you take five core courses, the remaining courses may be chosen from the list of recommended electives below (or you may continue with core courses).

While students are encouraged to take one of the recommended electives, a maximum of two courses may be taken outside of data science as long as they are offered through the MSOL program. Our recommended electives include:

Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisite: course 118 or equivalent. Focus on emerging and state-of-art Internet of Things (IoT) technologies and their applications. Covers diverse set of IoT and wireless networking technologies such as millimeter wave (mmWave), acoustic, radio-frequency identification (RFID), Wi-Fi, long-range (LoRa), Bluetooth, global positioning system (GPS) for a variety of emerging communication and sensing applications such as 5G, digital medicine, digital farming, smart cities and smart homes. Students learn how to design and build IoT systems. Letter grading.
Lecture, four hours; outside study, eight hours. Recommended requisites: COM SCI 143. This course is designed for graduate students. The scale of web data requires novel algorithms and principles for their management and retrieval. This course introduces the study of web characteristics and new management techniques needed to build computer systems suitable for the web environment. Topics include web measuring techniques, large-scale data mining algorithms, efficient page refresh techniques, web search ranking algorithms and query processing techniques on independent data sources. Letter grading.
Lecture, four hours; discussion, one hour; outside study, 10 hours. Requisites: EC ENGR 102 (enforced), MATH 32B, 33B. This course introduces the basic concepts of probability, including random variables and vectors, distributions and densities, moments, characteristic functions and limit theorems. Topics include applications to communication, control and signal processing and an introduction to computer simulation and generation of random events. Letter grading.
(Same as Bioengineering M214A.) Lecture, three hours; laboratory, two hours; outside study, seven hours. Requisite: EC ENGR 113. Topics include the theory and applications of digital processing of speech signals, mathematical models of human speech production and perception mechanisms and speech analysis/synthesis. Techniques include linear prediction, filter-bank models and homomorphic filtering. There are also applications to speech synthesis, automatic recognition and hearing aids. Letter grading.
Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: EC ENGR 131A or equivalent. This course introduces students to the research developments and new mathematical techniques for emerging large-scale, ultra-reliable, fast and affordable data storage systems. Topics include, but are not limited to, graph-based codes and algebraic codes and decoders for modern storage devices (e.g., Flash), rank modulation, rewriting codes, algorithms for data deduplication and synchronization and redundant array of independent disks (RAID) systems. Letter grading.
Lecture, four hours; discussion, one hour; outside study, seven hours. Students get insight into special topics in one or more aspects of signals and systems, such as communications, control, image processing, information theory, multimedia, computer networking, optimization, speech processing, telecommunications and VLSI signal processing. May be repeated for credit with topic change. S/U or letter grading.
Preparation: one undergraduate linear algebra course. Designed for first-year graduate students in all branches of engineering, science, and related disciplines. Introduction to matrix theory and linear algebra, language in which virtually all of modern science and engineering is conducted. Review of matrices taught in undergraduate courses and introduction to graduate-level topics. Letter grading.

Comprehensive Exam

Students can meet the Comprehensive Exam Requirement in two ways:

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. Letter grading.
The written exam questions are held concurrently with the final exam of the graduate-level courses. Students may select which exams they would like to count towards the Comprehensive Exam Requirement.

Request Information

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